1 Introduction

In the face of unprecedented challenges posed by a growing global population and escalating urbanization, agriculture stands at a critical crossroads. The imperative to sustainably produce food, fiber, and biofuels while conserving resources and mitigating climate change has become paramount [1,2,3]. Traditional agricultural methods, once relied upon for generations, are now facing scrutiny due to their limited capacity to meet the demands of modern agriculture sustainably. While traditional methods may hold cultural significance and, in some cases, demonstrate sustainability, they often lack the efficiency and scalability required to feed a rapidly growing population in an era of climate uncertainty. As such, the need to transition toward more advanced and technology-driven approaches has become increasingly apparent.

It is within this crucible of necessity that precision agriculture (PA) emerges as a beacon of hope and a catalyst for change. PA represents a transformative departure from conventional farming practices, acknowledging the inherent variability within agricultural ecosystems and harnessing cutting-edge technologies to optimize every facet of farming [4,5,6,7]. Its holistic approach aims to achieve more with less—enhancing yields, resource efficiency, and environmental sustainability across various sectors including farming, livestock management, and forestry.

This paper embarks on a comprehensive exploration of precision agriculture, driven by three fundamental questions:

  1. 1.

    What Are the Tangible Benefits of Precision Agriculture? The paper investigates the concrete advantages that precision agriculture brings to farmers, including in crop farming, livestock production, resource efficiency, sustainability, and enhanced profitability. Through real-world case studies, the paper illustrates how these benefits are realized in various farming scenarios.

  2. 2.

    What Are the Primary Challenges? The study examines the challenges that precision agriculture faces, including data security and privacy concerns, the cost of implementation, the need for education and training, evolving regulatory frameworks, and ensuring long-term environmental sustainability. These challenges must be addressed to unlock the full potential of precision agriculture.

  3. 3.

    What Are the Promising Future Directions? The study explores the potential future directions and solutions that can drive precision agriculture's widespread adoption and success. This includes strategies to enhance data security, make precision agriculture more affordable and accessible, provide comprehensive education and training, update regulatory frameworks, ensure environmental sustainability, and improve integration and interoperability among precision agriculture tools.

Structured into nine sections, this paper offers a systematic exploration of precision agriculture. It begins with a methodology outlining the systematic approach employed in reviewing and synthesizing existing literature (Sect. 2), followed by a historical evolution of precision agriculture (Sect. 3). Section 4 explores how the data-driven revolution has reshaped agricultural practices, while Sect. 5 highlights the cutting-edge technologies at the forefront of precision agriculture. Sections 6, 7, and 8 delve into the tangible benefits, real-world case studies, and challenges faced by precision agriculture, respectively. Finally, Sect. 9 offers a concise conclusion, summarizing the key findings and emphasizing the importance of overcoming challenges to unlock the full potential of precision agriculture.

2 Methodology: review process

The methodology employed in this review paper involved a systematic and comprehensive literature search to identify relevant studies, articles, and case studies about precision agriculture and its impact on sustainable farming practices. The following steps outline the methodology adopted:

  1. 1.

    Literature search: A thorough search was conducted across various academic databases, including but not limited to PubMed, IEEE Xplore, ScienceDirect, and Google Scholar. The search was conducted from January 2015 to March 2024 to capture the most recent literature. Keywords such as “precision agriculture,” “sustainable farming,” “AI in agriculture,” and “IoT in farming” were used to ensure a broad and inclusive search yielding a total of 259 peer-reviewed articles and case studies.

  2. 2.

    Inclusion and exclusion criteria: The initial search results were screened based on predefined inclusion and exclusion criteria. Studies considered for inclusion focused on the application of precision agriculture technologies, including AI, machine learning, sensors, and IoT, in the context of sustainable farming practices. The exclusion criteria involved studies not directly related to precision agriculture or those lacking relevance to sustainability. Only peer-reviewed articles and case studies published between January 2015 and March 2024 were included.

  3. 3.

    Data extraction: Relevant information from selected studies, including key findings, methodologies applied in the original research, and case study details, was extracted. Emphasis was placed on tangible benefits, challenges, and future directions in precision agriculture.

  4. 4.

    Synthesis of information: The extracted data were synthesized to provide a cohesive narrative on the applications, benefits, challenges, and future directions of precision agriculture in sustainable farming—the synthesis aimed to capture the diversity of applications across different agricultural domains and geographical locations.

  5. 5.

    Critical analysis: The included studies were critically analyzed to assess the methodological rigor of the original research. This involved evaluating the study design, data collection methods, and the reliability of reported results. Studies were also assessed for their relevance and contribution to the overarching theme of precision agriculture and sustainability.

  6. 6.

    Framework development: To present the findings coherently, a thematic framework was developed, categorizing the information into key sections such as applications of precision agriculture, tangible benefits, challenges faced, and future directions. This framework served as a guide to organize and structure the review paper effectively.

The systematic and structured approach to the literature review process ensures that the information presented in this paper is comprehensive, reliable, and representative of the current knowledge on precision agriculture and sustainable farming practices.

3 Historical evolution of precision agriculture

This section delves into the roots of precision agriculture, tracing its historical evolution. From the early use of GPS technologies to the contemporary integration of AI-driven systems, this historical exploration provides a context for understanding the trajectory of precision agriculture and its pivotal milestones.

3.1 Early beginnings

In the 1980s, a pioneering spirit emerged among farmers and researchers as they sought to challenge the limitations of traditional farming practices. This period saw the introduction of transformative technologies such as GPSs (Global Positioning Systems), GISs (Geographic Information Systems), yield monitors, and other data generators, heralding the dawn of precision agriculture [5, 8]. These technologies offered unprecedented levels of accuracy in positioning and data collection, allowing farmers to move beyond uniform resource application methods towards more nuanced and site-specific approaches.

One of the foundational concepts that emerged during this period was variable rate application (VRA), which represented a departure from the one-size-fits-all approach to inputs such as fertilizers and pesticides. Early adopters of VRA recognized the potential to tailor input application based on the specific needs of different areas within a field, leading to more efficient resource utilization and improved crop performance [6, 9, 10]. While initial systems required manual adjustments, they laid the groundwork for the development of more sophisticated precision agriculture technologies.

3.2 Advancements in the digital age

The transition to the digital age heralded a transformative era for precision agriculture, revolutionizing farming practices and paving the way for unprecedented levels of efficiency and productivity. Central to this evolution was the integration of GPS technology into farming equipment, which revolutionized field operations by offering precise positioning and mapping capabilities. With GPS-enabled systems, farmers could precisely delineate field boundaries, create accurate yield maps, and navigate their equipment with unparalleled accuracy [5, 11, 12].

This newfound precision laid the groundwork for the development of advanced planting systems that optimized seed placement for maximum yield potential. By precisely positioning seeds at optimal depths and intervals, farmers could achieve more uniform crop stands and maximize yield potential across their fields. Studies have demonstrated significant increases in crop yields and improvements in resource utilization attributed to the adoption of precision planting technologies [13,14,15,16,17].

Furthermore, the integration of sensor technology into farm equipment ushered in an era of real-time data collection and monitoring. Sensors embedded within machinery provided farmers with crucial insights into soil conditions, moisture levels, nutrient content, and crop health, allowing for data-driven decision-making in the field [18,19,20]. Real-time monitoring enabled farmers to respond promptly to changing conditions, optimizing inputs and interventions to maximize productivity while minimizing environmental impact.

The advent of wireless connectivity further accelerated the digitization of precision agriculture, facilitating seamless data transfer between farm machinery and central databases. This connectivity empowered farmers with access to vast amounts of data collected from sensors and monitoring systems, enabling sophisticated analytics and decision support tools [19, 21, 22]. By leveraging this data-rich environment, farmers could optimize their operations in real-time, adjusting planting, irrigation, fertilization, and pest management practices to maximize yields and profitability while minimizing resource inputs.

These advancements represented a paradigm shift from traditional farming practices reliant on manual adjustments to digitally informed, precision-driven approaches. The integration of GPS, sensor technology, and wireless connectivity laid the foundation for the next phase of precision agriculture, setting the stage for the exploration of cutting-edge technologies and innovative practices in the subsequent sections. Through continuous innovation and adaptation, precision agriculture continues to evolve, offering new opportunities to enhance sustainability, resilience, and productivity in agriculture.

3.3 Transition to agriculture 4.0—smart farming

The integration of advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), big data analytics, robotics, and blockchain has propelled precision agriculture into a new era often referred to as Agriculture 4.0 or smart farming [5, 8, 10, 23]. These transformative technologies empower autonomous decision-making, task automation, and the implementation of advanced sustainability practices, ushering in a new era of agricultural innovation. Agriculture 4.0 prioritizes sustainability, resilience, and regenerative practices, leveraging advanced technologies to optimize resource use and minimize environmental impact [5, 23, 24].

Furthermore, the emergence of Agriculture 5.0 envisions a future where farming systems are deeply integrated with concepts such as circular economy, regenerative agriculture, and biodiversity conservation [8, 25]. Agriculture 5.0 represents a holistic approach to farming that emphasizes symbiotic relationships between agricultural ecosystems and the environment, aiming to create regenerative and self-sustaining farming systems. This forward-looking vision aligns with findings from recent studies highlighting the interconnectedness of agricultural practices with broader ecological and societal contexts [5, 23, 25, 26].

As precision agriculture evolves towards Agriculture 5.0, it holds immense promise for revolutionizing farming practices and addressing the challenges of the twenty-first century in a sustainable and equitable manner. By embracing cutting-edge technologies and innovative approaches, precision agriculture is poised to enhance productivity, reduce environmental footprint, and promote social inclusivity in agricultural systems [5, 23,24,25, 27]. This transition underscores the transformative potential of precision agriculture and its pivotal role in shaping the future of agriculture, in line with the findings of recent research emphasizing the need for sustainable and resilient agricultural systems.

This overview of the historical evolution of precision agriculture underscores its transformative potential and sets the stage for exploring the cutting-edge technologies and innovative approaches driving its continued advancement. From its humble beginnings to its current state of advanced digitalization, precision agriculture represents a paradigm shift in agricultural practices and holds the key to a more sustainable and resilient agricultural future. In the subsequent sections, we will delve deeper into these technologies, examining how they refine and expand the capabilities of precision agriculture to address the evolving needs of the agricultural sector in alignment with the findings of recent research.

4 The data-driven revolution

4.1 The type of data sources in precision agriculture

In the realm of precision agriculture, the significance of data cannot be overstated. Data is the lifeblood of this approach, enabling farmers to navigate the complexities of modern agriculture with precision and insight [17, 28]. Precision agriculture systems rely on data from various sources, each contributing valuable information about the state of the farm and its environment.

Soil Data: Soil sensors deployed in the field provide real-time measurements of moisture levels, temperature, nutrient content, and pH [29, 30]. This information empowers farmers to tailor irrigation and fertilization strategies to the specific needs of each area within a field. Farmers can reduce waste and enhance resource efficiency by avoiding the over-application of resources in areas that are already adequately supplied, addressing deficiencies precisely where needed and enhancing overall farm productivity. Moreover, advancements in soil sensing technologies continue to enhance the accuracy and reliability of soil data collection. Recent studies have highlighted the efficacy of emerging sensor technologies in providing high-resolution soil data, allowing for more precise and targeted agricultural interventions [31,32,33,34]. On the other hand, the integration of machine learning algorithms with soil sensors, as demonstrated by some researchers [35,36,37], holds promise in predicting soil health and nutrient requirements with greater accuracy, thereby further optimizing resource management practices in precision agriculture. These developments underscore the evolving landscape of soil data collection and its pivotal role in driving sustainable agricultural practices.

Weather Data: Real-time weather data, including temperature, humidity, wind speed, and precipitation, is pivotal in making informed decisions in precision agriculture [38,39,40]. It enables farmers to anticipate weather-related challenges such as droughts, heavy rainfall, or frost events. Weather data is precious for optimizing planting schedules, irrigation management, and disease forecasting. For instance, anticipating an impending drought based on weather forecasts allows farmers to adjust irrigation schedules, ensuring optimal water usage and crop health. Similarly, advanced weather monitoring facilitates the optimization of planting schedules, enabling farmers to align planting activities with favorable weather conditions for optimal crop growth and development.

Moreover, weather data serves as a critical input for disease forecasting models in precision agriculture [41, 42]. By analyzing historical weather patterns and current atmospheric conditions, farmers can predict the onset and spread of crop diseases more accurately. Timely disease forecasts empower farmers to implement preventive measures such as targeted pesticide applications or crop rotations, minimizing yield losses and preserving crop health.

Recent advancements in weather monitoring technologies, such as remote sensing and satellite imagery, have further enhanced the accuracy and accessibility of weather data in precision agriculture [32, 43, 44]. These technologies offer farmers comprehensive and up-to-date weather information at various spatial and temporal scales, enabling them to make data-driven decisions that optimize agricultural practices and maximize productivity while mitigating risks associated with adverse weather conditions.

Crop Performance Metrics: Continuous crop health and growth monitoring is possible through sensors and remote sensing technologies [14, 45]. These tools can assess chlorophyll content, leaf temperature, and canopy development. By tracking these metrics, farmers gain insights into potential issues such as nutrient deficiencies or pest infestations, allowing for timely intervention. For example, variations in chlorophyll content measured by sensors can signal nutrient deficiencies, prompting farmers to adjust fertilization strategies accordingly. Similarly, changes in leaf temperature may indicate the presence of pests or diseases, allowing farmers to take prompt action to mitigate potential damage.

Moreover, remote sensing technologies offer a broader perspective by providing comprehensive imagery of crop fields from aerial or satellite platforms [37]. This imagery enables farmers to assess crop health and development at a larger scale and identify areas of concern that may not be apparent from ground-level observations alone. By analyzing remote sensing data, farmers can detect spatial variations in crop performance and implement targeted interventions to address specific issues, optimizing resource allocation and maximizing yields.

On the other hand, the integration of crop performance metrics with advanced analytics and machine learning algorithms enables predictive modeling of crop growth and yield potential [28, 46, 47]. By analyzing historical data and current performance metrics, these models can forecast future crop performance under different scenarios, helping farmers make informed decisions and optimize production strategies for maximum efficiency and profitability. Overall, crop performance metrics serve as a cornerstone of precision agriculture, empowering farmers with actionable insights to enhance crop productivity and sustainability.

Historical Data: Beyond real-time data, historical records are equally valuable [48, 49]. Trends over multiple seasons provide insights into the long-term performance of fields, assisting farmers in making decisions about crop rotations, soil management, and pest control strategies. For example, trends in crop yields and soil nutrient levels over several years can inform decisions about optimal planting schedules and nutrient management practices [50]. Similarly, historical records of pest infestations and disease outbreaks can guide the development of proactive pest management strategies to mitigate future risks [51,52,53].

Furthermore, historical data serves as a valuable tool for evaluating the effectiveness of past management practices and identifying areas for improvement. By comparing historical performance metrics with current data, farmers can assess the impact of different interventions and adjust their practices accordingly to optimize outcomes [48, 54].

Moreover, historical data plays a crucial role in risk assessment and resilience planning. By understanding past patterns of weather variability, crop performance, and pest pressure, farmers can anticipate potential challenges and develop strategies to mitigate risks and minimize losses [55, 56]. This proactive approach to risk management enhances the resilience of agricultural systems and improves overall farm sustainability. Overall, historical data provides a valuable foundation for decision-making in precision agriculture, enabling farmers to leverage past experiences and trends to optimize current practices and enhance the long-term productivity and sustainability of their operations.

Remote sensing data: In addition to soil, weather, crop performance metrics, and historical data, another crucial type of data source in precision agriculture is remote sensing data. Remote sensing technologies, such as satellites, drones, and aerial imaging, provide valuable insights into crop health, growth patterns, and environmental conditions [48]. These technologies capture high-resolution images of agricultural landscapes, allowing farmers to monitor crop development, detect anomalies, and assess field variability with unprecedented accuracy [37, 47, 57, 58]. Remote sensing data can identify areas of stress, such as water deficiency or pest infestation, enabling farmers to target interventions precisely and optimize resource allocation [59, 60]. Moreover, the integration of remote sensing data with advanced analytics techniques, such as machine learning algorithms, enhances the predictive capabilities of precision agriculture systems, enabling proactive decision-making and risk management [43, 47]. By harnessing remote sensing data, farmers can gain a comprehensive understanding of their fields' dynamics and make informed decisions to maximize productivity and sustainability.

In conclusion, precision agriculture relies on a diverse array of data sources, including soil, weather, crop performance metrics, historical data, and remote sensing data, to inform decision-making and optimize farm management practices. These data sources provide valuable insights into soil conditions, weather patterns, crop health, and historical trends, enabling farmers to tailor interventions to the specific needs of their fields. By leveraging these data sources, farmers can enhance resource efficiency, reduce environmental impact, and improve overall farm productivity and sustainability. As precision agriculture continues to evolve, advancements in data collection, analytics, and technology integration will further enhance its capabilities, paving the way for a more efficient, resilient, and sustainable agricultural future.

4.2 Data integration and analysis

The true power of data in precision agriculture comes to fruition through integration and analysis [61,62,63]. Modern precision agriculture systems have advanced analytics and machine learning capabilities to process and interpret vast datasets near-real-time. This transformative capability empowers farmers to extract actionable insights from their collected data.

Data Integration: Integration of data from various sources, including soil sensors, weather stations, crop sensors, and more, creates a cohesive and comprehensive view of the farm [64, 65]. This integration enables farmers to correlate different datasets, revealing complex relationships between soil conditions, weather patterns, and crop performance. For instance, machine learning algorithms can analyze historical weather data and crop performance metrics to forecast future yield outcomes and recommend optimal planting schedules or irrigation strategies.

In essence, data integration and analysis serve as the cornerstone of modern precision agriculture, enabling farmers to harness the full potential of available data resources. By integrating diverse datasets and applying advanced analytics, farmers can make informed decisions that enhance agricultural sustainability, resilience, and profitability. As technology continues to evolve and data-driven insights become more sophisticated, the role of data integration and analysis in precision agriculture will only grow in significance, driving continuous innovation and advancement in agricultural practices.

Machine Learning and AI: Machine learning algorithms and artificial intelligence (AI) stand at the forefront of data analysis in precision agriculture [5, 20, 46]. AI encompasses a broad range of techniques that enable machines to mimic human cognitive functions, such as learning, reasoning, and problem-solving. Machine learning, a subset of AI, focuses on the development of algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed. These technologies can identify patterns and trends that may elude human observers. For example, machine learning models can predict disease outbreaks by analyzing historical disease data alongside current weather conditions. Such predictive capabilities of machine learning and AI empower farmers to take preemptive actions, such as targeted pest control or adjusting irrigation schedules.

In addition to predictive analytics, machine learning and AI also excel in optimizing operational efficiency and resource management in precision agriculture. Through continuous analysis of real-time data streams from various sensors and monitoring devices, these technologies can dynamically adjust farming practices to maximize productivity while minimizing resource inputs [46, 52, 66]. For example, AI-driven irrigation systems can autonomously regulate water usage based on soil moisture levels, weather forecasts, and crop water requirements, ensuring optimal hydration without unnecessary waste. Similarly, machine learning algorithms can optimize fertilizer applications by analyzing soil nutrient levels, crop growth patterns, and historical yield data to tailor nutrient doses precisely to the needs of each area within a field. By harnessing the power of machine learning and AI, farmers can achieve unprecedented levels of precision, efficiency, and sustainability in their agricultural operations, driving the advancement of precision agriculture into the digital age.

Optimized Decision-Making: Data-driven insights directly inform decision-making processes within precision agriculture, empowering farmers to make informed and strategic choices at every stage of the farming cycle [39, 66, 67]. By leveraging real-time data streams and advanced analytics, farmers can optimize planting schedules, determine the ideal time for harvest, adjust irrigation rates based on soil moisture levels, and fine-tune nutrient applications. For example, the analysis of soil moisture data allows farmers to adjust irrigation schedules, ensuring that crops receive the optimal amount of water without wastage. Similarly, data on crop health and growth patterns inform decisions regarding the timing of planting and harvesting, enabling farmers to capitalize on optimal conditions for maximum yield [43, 52]. Furthermore, nutrient management strategies can be optimized through data-driven insights, with farmers tailoring fertilizer applications to the specific needs of different areas within a field based on soil nutrient levels and crop requirements. By harnessing the power of data-driven decision-making, farmers can enhance resource utilization, minimize environmental impact, and ultimately maximize crop yields, ushering in a new era of precision agriculture characterized by efficiency, sustainability, and innovation.

In essence, data integration and analysis elevate precision agriculture beyond the realm of data collection into a realm of informed decision-making. By synthesizing data from diverse sources such as soil sensors, weather stations, and crop monitoring devices, farmers gain a holistic understanding of their agricultural ecosystems. This comprehensive view enables them to uncover hidden patterns, correlations, and trends, unlocking new opportunities for optimization and efficiency. Equipped with these insights, farmers can navigate the dynamic challenges of farming with confidence, making strategic decisions that maximize resource utilization, minimize environmental impact, and ultimately enhance productivity and profitability. In essence, data integration and analysis catalyze a paradigm shift in agriculture, ushering in an era of precision, sustainability, and innovation.

As we progress in this paper, we will delve into the emerging technologies at the forefront of precision agriculture, including sensors, unmanned aerial vehicles (UAVs), and artificial intelligence. These technologies represent the cutting edge of data collection and analysis, pushing the boundaries of what is possible in modern farming practices.

5 Emerging technologies in precision agriculture

5.1 Sensors and internet of things (IoT)

Sensors, integral components of modern precision agriculture, form the foundation of what is known as the Internet of Things (IoT) in agriculture [5, 67]. These devices are strategically positioned across fields to collect real-time data on various environmental factors continuously. The Internet of Things refers to the interconnected network of these sensors, which communicate with each other and with central data processing systems. Through this network, farmers gain real-time access to critical information about soil moisture, temperature, humidity, crop health, and more, all of which are essential for making informed decisions. In this section, we delve into the principles of operation, potential applications of these sensors, and the specific benefits they offer.

The deployment of soil sensors stands as a testament to their vital role. Soil moisture sensors, temperature probes, and nutrient probes are embedded in the ground at various depths to provide continuous readings. This data informs irrigation decisions, enabling farmers to apply water precisely when and where it is needed, reducing overwatering and conserving resources[68, 69]. This targeted approach minimizes the risk of overwatering, conserves water resources, and promotes efficient water usage. For instance, in a research study conducted in California's almond orchards, soil moisture sensors were utilized to monitor soil conditions continuously. The findings of this study revealed a remarkable 33% reduction in water usage while maintaining almond yields at optimal levels [70]. Such outcomes underscore the practical significance of soil sensors in driving sustainable agricultural practices and resource conservation efforts.

Weather stations with sensors that measure various meteorological parameters such as temperature, humidity, wind speed, rainfall, and solar radiation are another integral component of precision agriculture. These stations serve as invaluable tools for collecting real-time data, which is instrumental in weather forecasting and decision-making processes. By analyzing the data gathered from weather stations, farmers can optimize planting schedules, anticipate weather fluctuations, and prepare effectively for adverse weather conditions. For example, in viticulture, vineyard managers rely on weather stations to accurately predict frost events. By leveraging this predictive capability, they can implement preventive measures to mitigate frost damage and safeguard grape yields [71, 72]. This demonstrates the indispensable role of weather stations in enhancing agricultural resilience and optimizing crop management practices.

Crop sensors, whether mounted on agricultural machinery or drones, serve as invaluable tools in providing real-time assessments of crop health and growth parameters. These sensors are designed to measure a range of critical plant characteristics, including chlorophyll content, leaf temperature, and biomass. By continuously monitoring these metrics, farmers can swiftly identify signs of stress or nutrient deficiencies in their crops, enabling proactive intervention to mitigate potential yield losses. For example, crop sensors were deployed in the agricultural fields to evaluate nitrogen stress levels in corn crops. Through careful analysis of the sensor data, farmers were able to fine-tune their nitrogen fertilizer applications precisely to the needs of the crops. This targeted approach resulted in a remarkable 50% reduction in nitrogen usage while still maintaining optimal corn yields, showcasing the effectiveness of crop sensors in optimizing resource utilization and enhancing crop productivity [14, 73].

The data collected by these sensors is seamlessly transmitted to centralized databases or cloud platforms, enabling real-time monitoring and remote access. This connectivity empowers farmers to make informed decisions, regardless of their physical location. By leveraging IoT sensors in precision agriculture, farmers can optimize resource utilization, enhance crop health, reduce operational costs, and minimize environmental impact. Research conducted by [52, 74] underscores the significant benefits of employing IoT sensors, highlighting their role in driving efficiency and sustainability within agricultural practices.

5.2 Unmanned aerial vehicles (UAVs) and remote sensing

Unmanned Aerial Vehicles, commonly known as drones, have transformed data collection in precision agriculture. Equipped with sophisticated sensors and cameras, UAVs are capable of capturing high-resolution imagery and data across large agricultural areas. The principles of operation, potential applications, and specific benefits of UAVs in precision agriculture are discussed below.

Aerial imagery captured by drones provides a bird's-eye view of fields, making it invaluable for creating detailed field maps and monitoring crop health. This imagery is instrumental in identifying areas of concern, such as pest infestations or water stress, enabling farmers to take proactive measures. By detecting issues early and precisely targeting treatments, farmers can significantly reduce pesticide use, minimize crop damage, and enhance the overall sustainability of their farming practices [32, 69]. For instance, in cotton fields, drones equipped with high-resolution cameras have been utilized to identify factors contributing to yield variability, thereby enabling farmers to implement targeted interventions and optimize crop yields [75, 76].

Remote sensing capabilities, such as multispectral cameras and LiDAR sensors, contribute to data-driven decision-making in precision agriculture. Multispectral cameras can detect subtle variations in crop reflectance, indicating stress, nutrient deficiencies, or disease outbreaks. This early detection capability is invaluable for farmers as it allows them to identify and address issues promptly during the growing season, thereby optimizing crop health and maximizing yields [77, 78]. In addition, LiDAR sensors facilitate the creation of precise elevation models of fields, offering valuable insights into terrain characteristics. Farmers can leverage this information for various purposes, such as planning drainage systems, analyzing terrain for optimal water flow, and understanding topographical variations that may impact crop health [79].

The European Space Agency’s (ESA) “FertilEyes” project serves as a compelling illustration of the transformative power of remote sensing within precision agriculture. Through the utilization of unmanned aerial vehicles (UAVs) equipped with multispectral cameras, the project meticulously assessed nutrient levels in wheat fields [80]. By scrutinizing reflectance patterns captured by these advanced imaging systems, the project successfully showcased the feasibility of implementing precise nitrogen management strategies. This innovative approach not only facilitated a reduction in nitrogen application but also ensured optimal wheat crop yields. Furthermore, ESA, in collaboration with GOTS and the Marple company, recently pioneered a groundbreaking initiative utilizing remote monitoring for organic cotton certification. Leveraging cutting-edge AI algorithms and satellite imagery, this project distinguishes between organic and non-organic cotton fields, marking a significant advancement in agricultural certification processes. These initiatives underscore the indispensable role of remote sensing technologies in fostering sustainable and efficient agricultural practices.

In summary, UAVs and remote sensing offer several advantages in precision agriculture, including early issue detection, precision targeting of treatments, and data-driven decision-making, leading to more efficient, sustainable, and profitable farming practices.

5.3 Artificial intelligence (AI) and machine learning

In recent years, the agricultural sector has undergone a significant transformation with the widespread adoption of artificial intelligence (AI) and machine learning applications. One notable trend is the integration of edge computing with AI in farming practices, enabling faster decision-making through localized data processing [5, 81, 82]. This integration holds immense potential for real-time applications in precision agriculture, thereby enhancing the efficiency of data analysis processes.

Furthermore, there have been notable breakthroughs in computer vision and sensor technologies, leading to the development of sophisticated monitoring systems that utilize high-resolution imaging, including multispectral and hyperspectral data. These advancements offer a detailed perspective on crop health and environmental conditions, facilitating more accurate disease detection and improved overall crop management strategies.

Moreover, collaborative and open-source initiatives have emerged as key drivers of innovation in the agricultural AI and machine learning landscape. Global communities are actively sharing knowledge and contributing to the development of open-source tools, democratizing access to cutting-edge technologies for farmers worldwide, regardless of their location or farm size. This collaborative approach not only accelerates innovation but also fosters inclusivity within the agricultural technology ecosystem, empowering farmers with the tools and resources needed to thrive in today's rapidly evolving agricultural landscape.

Artificial intelligence (AI) and machine learning have emerged as powerful tools for processing and analyzing vast datasets, extracting meaningful patterns and insights, and making predictions or recommendations based on incoming data. These technologies find numerous applications in precision agriculture, as discussed below.

Crop disease prediction stands as a prime example of the remarkable promise that artificial intelligence (AI) and machine learning hold for precision agriculture [43, 51, 53]. By leveraging historical data on crop diseases and considering various environmental factors such as weather conditions, soil moisture levels, and pest populations, machine learning models can accurately forecast disease outbreaks. These models identify intricate patterns within the data, empowering farmers to implement targeted pest control measures and proactive disease management strategies.

A notable instance of this application is the development of an AI-based system tailored to predict Fusarium head blight (FHB) outbreaks in wheat fields [83, 84]. This system demonstrated exceptional accuracy in forecasting FHB occurrences, enabling farmers to swiftly and precisely apply fungicides when needed. As a result, crop losses were minimized, and disease management practices were significantly improved, showcasing the tangible benefits of AI-driven disease prediction in enhancing crop health and productivity.

Yield prediction stands as another critical application of artificial intelligence (AI) and machine learning within precision agriculture [85, 86]. These advanced algorithms utilize a wealth of data, including historical yield records, weather forecasts, soil characteristics, and crop growth stages, to forecast crop yields with remarkable accuracy. By leveraging this comprehensive dataset, AI-driven yield prediction models provide farmers with valuable insights essential for harvest planning, resource allocation, and informed marketing decisions.

A noteworthy study conducted at Iowa State exemplifies the potential of machine learning in yield prediction, particularly focusing on soybean and corn crops [73, 87]. Through the development of a sophisticated machine learning model, researchers were able to generate precise predictions of soybean yields. These insights equip farmers with actionable information, enabling them to optimize their farming practices, adjust harvesting schedules, and effectively manage resources for maximum productivity.

Resource optimization stands as a central focus of AI-driven models in precision agriculture. These sophisticated models autonomously optimize resource allocation by considering a multitude of variables, including soil properties, weather forecasts, and crop growth stages. By leveraging this diverse array of data, AI-driven systems can make informed decisions to maximize efficiency and minimize waste.

A compelling example of this application is found in a Napa Valley vineyard, where an AI-driven irrigation management system was put to the test. This system autonomously adjusted irrigation schedules based on real-time soil data, weather forecasts, and crop water requirements. The results were remarkable, with the vineyard achieving a 30% reduction in water use while simultaneously maintaining vineyard health. This demonstrates the power of AI in optimizing resource allocation, ensuring sustainable farming practices that benefit both the environment and the bottom line [88].

Indeed, AI and machine learning play integral roles in precision agriculture, revolutionizing farming practices and paving the way for sustainable and profitable operations. By harnessing the power of these technologies, farmers can make timely decisions, optimize resource allocation, and ultimately enhance crop yields and profitability.

Research conducted by [74, 89,90,91,92] underscores the transformative impact of AI and machine learning in agriculture. Their findings highlight how these technologies empower farmers to navigate the complexities of modern farming, from optimizing planting schedules to managing irrigation and fertilization practices. Through AI-driven insights, farmers can fine-tune their operations with precision, maximizing productivity while minimizing environmental impact.

In today's competitive agricultural landscape, where efficiency and sustainability are paramount, AI and machine learning offer indispensable tools for success. By leveraging these technologies, farmers can stay ahead of the curve, adapting to changing conditions and ensuring the long-term viability of their operations. As such, the integration of AI and machine learning into precision agriculture represents a pivotal step towards a more resilient and prosperous future for farmers worldwide.

6 Realizing the tangible benefits

6.1 Increased crop yields

Precision agriculture significantly boosts crop yields through precise resource management, optimized planting practices, and early intervention. Real-world examples, such as studies at Iowa State, demonstrate substantial improvements in soybean and corn yields using machine learning models. These models, incorporating diverse data sets, provide valuable insights that contribute to enhanced harvests [73, 87]. Similar success stories across crops and regions underscore the effectiveness of precision agriculture in achieving increased productivity. This increase in productivity is achieved through a combination of factors:

Optimized Resource Use: Precision agriculture enables farmers to apply resources (such as fertilizers, pesticides, and water) accurately. Farmers can ensure that crops receive the right amount of nourishment at the right time by tailoring these inputs to match the specific needs of different areas within a field. This precision minimizes resource waste and maximizes the growth potential of each plant [93]. Studies have demonstrated that by reducing input waste through precision farming, farmers can save up to 20% on fertilizer costs and at least 20% on water usage, contributing directly to farm profitability [94, 95]. This economic benefit is especially significant in regions facing water scarcity, where optimized water use not only boosts yields but also cuts costs.

For instance, studies conducted by [96,97,98] in vineyard settings have demonstrated significant reductions in water usage without compromising crop yield or quality. By tailoring irrigation schedules based on real-time soil data and weather forecasts, farmers can optimize water usage, mitigate environmental impact, and enhance overall farm sustainability. By adopting site-specific management practices, farmers can address nutrient deficiencies, pest outbreaks, and weed infestations more effectively, leading to healthier crops and higher yields. Overall, optimized resource use through precision agriculture not only benefits farmers by improving efficiency and profitability but also contributes to environmental conservation and sustainability in agriculture.

Improved Planting Practices: Precision planting technologies driven by GPS and automation represent a significant advancement in modern agriculture, offering farmers precise control over the planting process. By leveraging GPS guidance systems, farmers can ensure that seeds are planted at optimal depths and spacing, resulting in uniform crop emergence and reduced competition among plants. This precise planting enhances crop uniformity and promotes the establishment of healthy, productive stands.

Research conducted by [12, 14, 59] highlights the tangible benefits of precision planting practices. Their studies demonstrate that by adopting precision planting technologies, farmers can achieve more consistent crop stands, leading to improved crop performance and higher yields. The economic impact is notable, with farmers reducing seed waste by up to 15% and achieving higher yields that contribute to better marketability and increased farm income [34, 99]. Additionally, by reducing seed waste and minimizing plant competition, precision planting contributes to resource efficiency and overall farm profitability.

Moreover, precision planting enables farmers to implement variable rate planting strategies, adjusting seed rates based on soil conditions, historical yield data, and other site-specific factors. This tailored approach optimizes seed placement and promotes uniform crop development across fields with varying soil characteristics or topographies. By achieving greater uniformity, farmers are able to command higher prices for premium quality crops, further enhancing their profitability. Overall, precision planting practices play a crucial role in maximizing crop productivity, enhancing farm sustainability, and ensuring food security in a rapidly changing agricultural landscape.

Timely Intervention: Timely intervention stands as a cornerstone of precision agriculture, facilitated by the early detection of potential issues through real-time data from sensors and drones. By continuously monitoring key parameters such as soil moisture, nutrient levels, and crop health, farmers can swiftly identify signs of nutrient deficiencies, pest infestations, or water stress. Armed with this actionable information, farmers can implement targeted interventions before problems escalate, mitigating crop losses and maintaining optimal crop conditions throughout the growing season.

The significance of timely intervention is underscored by studies conducted by [4, 6, 74, 100], which demonstrate the effectiveness of proactive management practices in precision agriculture. By leveraging real-time data and advanced monitoring technologies, farmers can respond promptly to emerging challenges, minimizing the impact of stressors on crop health and productivity. For instance, timely interventions based on real-time crop monitoring systems can prevent losses of up to 15% in cases of pest infestations or water stress, as demonstrated by trials in Brazilian soybean farms, where savings on crop protection chemicals led to a 10% reduction in operating costs [93, 101]. Moreover, the proactive nature of precision agriculture allows farmers to optimize resource allocation, reduce reliance on reactive treatments, and foster sustainable farming practices.

In essence, timely intervention represents a fundamental principle of precision agriculture, enabling farmers to stay ahead of potential threats and optimize crop performance in dynamic agricultural environments. By embracing proactive management strategies supported by real-time data insights, farmers can enhance resilience, minimize risk, and achieve more consistent and profitable yields.

Enhanced Crop Health: Enhanced crop health is a key outcome of precision agriculture, facilitated by the precise application of resources and adaptive management practices informed by real-time data. By optimizing the timing, quantity, and placement of inputs such as fertilizers, pesticides, and water, farmers can ensure that crops receive the necessary nutrients and protection while minimizing wastage and environmental impact. This targeted approach enables crops to develop robust root systems, stronger immune responses, and greater resilience to environmental stressors.

Research by [12, 51, 52, 91, 102] highlights the link between precision agriculture practices and enhanced crop health, emphasizing the role of data-driven decision-making in promoting plant vigor and resilience. By leveraging real-time data insights to fine-tune management strategies, farmers can address emerging challenges promptly, such as nutrient deficiencies, pest outbreaks, or water shortages. Proactive interventions, guided by precision agriculture principles, contribute to the overall health and vitality of crops, resulting in more consistent growth, improved disease resistance, and ultimately higher yields. In fact, the adoption of precision technologies has been shown to increase yields by 20–25% in certain crops, providing farmers with both environmental and economic benefits [99, 101, 103].

In conclusion, increased crop yields represent just one facet of the multifaceted benefits offered by precision agriculture. By leveraging advanced technologies and data-driven insights, farmers can optimize resource use, improve planting practices, intervene timely, and enhance crop health, all of which collectively contribute to higher productivity and profitability. However, the journey towards realizing the full potential of precision agriculture extends beyond yield improvements. It encompasses a broader commitment to resource efficiency, sustainability, and environmental stewardship. Transitioning from increased crop yields to resource efficiency and sustainability, we delve into how precision agriculture fosters responsible and resilient farming practices while minimizing environmental impact.

6.2 Resource efficiency and sustainability

In the realm of resource efficiency and sustainability, precision agriculture emerges as a powerful ally, aligning closely with sustainability goals and environmental stewardship principles. Through precise management practices and targeted interventions, precision agriculture endeavors to optimize resource utilization while minimizing waste and environmental impact. This subsection explores how precision agriculture achieves resource efficiency and sustainability through various strategies and technologies.

Conservation of Water: Optimized irrigation is crucial in regions facing water scarcity. Precision agriculture uses soil moisture sensors and data-driven irrigation systems to deliver water precisely where and when needed, mitigating waste and ensuring optimal hydration for crops. This proactive approach not only conserves water but also safeguards against over-irrigation, promoting sustainable water management practices [10, 74, 96, 104]. Research indicates that precision irrigation systems, especially in drought-prone areas like California, have cut water usage by at least 20% while increasing yields by 15%, resulting in annual savings in operation cost per acre [93, 99, 101].

Reduced Chemical Application: Precision agriculture minimizes the over-application of fertilizers and pesticides. By analyzing data on soil conditions, crop health, and pest populations, farmers can apply these inputs only when and where they are necessary. This reduction in chemical usage not only lowers the risk of environmental contamination but also fosters safer and more sustainable farming practices overall [52, 102]. Studies have demonstrated that precision farming practices can reduce pesticide and fertilizer costs by 10–30%, resulting in significant cost savings and improved profitability for farmers [5, 33, 94].

Soil Health Preservation: Sustainable soil management is a cornerstone of precision agriculture. By tailoring nutrient applications to specific soil conditions, farmers can maintain soil health and fertility [105]. Additionally, practices such as reduced tillage, which minimizes soil disturbance, help prevent erosion and preserve the long-term viability of agricultural land[106]. This holistic approach ensures that soil resources remain viable and productive for future generations of farmers.

Environmental Stewardship: Precision agriculture systems promote responsible land and resource management. By enabling farmers to minimize their environmental footprint through optimized resource use and reduced chemical applications, precision agriculture contributes to mitigating greenhouse gas emissions associated with farming activities. Moreover, by adopting precision agriculture techniques, farmers can protect local ecosystems by minimizing soil erosion, preserving biodiversity, and reducing pollution runoff into nearby water bodies. These efforts are in alignment with global sustainability objectives, demonstrating the crucial role of precision agriculture in promoting environmentally friendly farming practices and ensuring the long-term health of the planet.

6.3 Enhanced farm profitability

In addition to its environmental benefits, precision agriculture offers significant economic advantages. Farmers adopting precision agriculture technologies often see substantial cost reductions and increases in profitability. Studies have shown that farmers can experience a 15–30% improvement in profitability through reduced input costs, increased yields, and improved product quality [101, 107, 108].. This section explores how precision agriculture strategies and technologies drive economic gains for farmers, ultimately leading to improved financial outcomes and long-term sustainability.

Cost Reduction: Precision agriculture optimizes resource allocation, reducing input costs for fertilizers, pesticides, and water leads to significant savings for farmers. By leveraging precision agriculture technologies such as soil sensors, drones, and data analytics, farmers can precisely tailor their inputs—such as fertilizers, pesticides, and water—to match the specific needs of their crops and fields. This targeted approach minimizes waste and reduces over-application, resulting in lower input costs without compromising crop productivity.Studies have shown that implementing precision agriculture practices can lead to substantial reductions in production expenses, thereby improving farm profitability [93, 94].

Increased Market Value: In addition to cost reduction, precision agriculture also enhances farm profitability by increasing the market value of crops. The high-quality and uniformity achieved through precision agriculture practices translate to crops that command premium prices in the market. This is especially evident in fruits, vegetables, and specialty crops, where consumers value consistency in size, color, and overall quality.By leveraging technologies like precision planting and real-time monitoring, farmers can ensure that their crops meet stringent quality standards, thereby positioning them for higher returns in the marketplace. Studies have confirmed that adopting precision agriculture practices can lead to increased market value for agricultural produce, further bolstering farm profitability [9, 109].

Improved Decision-Making: The data-driven nature of precision agriculture provides farmers invaluable insights into their operations. By leveraging real-time information from various sources such as sensors, drones, and satellite imagery, farmers can make more informed decisions about resource allocation, planting schedules, and risk management. This data-driven approach enables farmers to optimize their operations, minimize risks, and capitalize on opportunities in a dynamic agricultural landscape. Studies have demonstrated that farmers who adopt precision agriculture practices experience improved decision-making processes, leading to better outcomes and ultimately, enhanced farm profitability [110,111,112].

Competitive Advantage: In a global agricultural landscape characterized by evolving market dynamics and environmental challenges, maintaining a competitive edge is paramount for farmers. Precision agriculture emerges as a pivotal tool in this pursuit, offering farmers the means to adapt and thrive amidst industry transformations. By harnessing advanced technologies and data-driven approaches, farmers can enhance their operational efficiency, minimize risks, and capitalize on emerging opportunities. Those who embrace precision agriculture gain a competitive advantage, positioning themselves for long-term success and ensuring the viability of their operations in a rapidly changing agricultural sector.

Data Monetization: In the realm of precision agriculture, an emerging trend is the concept of data monetization, presenting an opportunity for farmers to augment their income. By leveraging the wealth of data generated through precision agriculture practices, farmers can contribute to the advancement of the agricultural sector while potentially generating additional revenue. This involves sharing anonymized and aggregated data on crop performance, environmental conditions, and resource utilization with agricultural researchers, technology developers, or other stakeholders. The shared data serves as a valuable resource for refining existing technologies, developing innovative solutions, and gaining deeper insights into agricultural trends and practices. However, it is crucial to prioritize data privacy and security measures to ensure that farmers can participate confidently in data monetization initiatives without compromising sensitive information. By embracing responsible data-sharing practices, farmers not only optimize their own operations but also contribute to the broader progress of agriculture as a whole [113,114,115].

Precision agriculture is not merely a theoretical concept; it delivers concrete and substantial benefits to farmers and the agricultural industry as a whole. Increased crop yields, resource efficiency, sustainability, and enhanced profitability are tangible outcomes of this transformative approach to farming. In the subsequent sections of this paper, the paper delves into real-world case studies that showcase the practical applications and success stories of precision agriculture and the challenges that must be addressed to unlock its full potential.

7 Case studies in precision agriculture

This section explores real-world case studies that showcase precision agriculture's practical applications and success stories and the challenges that must be addressed to unlock its full potential. From precision livestock farming to crop management and beyond, these case studies provide valuable lessons and perspectives on the current state and future prospects of precision agriculture.

7.1 Precision livestock farming

Case Study 1 Smart Cattle Monitoring in Texas Ranches.

In Texas’s vast and sprawling cattle ranches, smart cattle monitoring has emerged as a game-changer. This technology is a prime example of precision livestock farming. Individual cattle are equipped with IoT sensors and GPS tracking devices, which serve as an extensive cattle health and management system. These sensors continuously collect and transmit data on various factors, including the cattle's location, body temperature, and feeding patterns.

One of the most significant benefits of this technology is its role in early disease detection. The sensors are programmed to recognize unusual increases in body temperature or deviations in grazing behavior. The system promptly alerts the rancher's smartphone when such anomalies are detected. This rapid notification enables ranchers to isolate and treat sick animals promptly, preventing the spread of disease within the herd. As a result, the mortality rate among cattle has significantly decreased, leading to healthier herds [116,117,118]. For instance, by utilized Live Care, an IoT-based healthcare framework for livestock in smart agriculture, to monitor 150 dairy cows, the system achieved a prediction accuracy of over 80% in identifying common cow diseases such as fever, cyst, mastitis, pneumonia, black quarter, and foot and mouth disease [117]. The infrastructure includes hardware devices, a cloud system, and an end-user framework, enabling effective tracking of dairy cow behavior and ensuring prompt identification of health issues.

Beyond disease management, the data collected by these smart cattle monitoring systems is leveraged for optimizing herd management strategies. Ranchers can make informed decisions about grazing patterns and feeding schedules by analyzing the data. This leads to healthier cattle and results in more efficient resource utilization, enhancing the ranch's overall sustainability [119, 120].

Moreover, precision livestock farming seamlessly aligns with modern supply chain requirements, particularly in producing high-quality meat products. Farmers can implement individualized care strategies by leveraging data-driven insights from IoT sensors and GPS tracking. This ensures optimal conditions for livestock, contributing to superior meat quality that meets stringent supply chain standards. The integration of precision practices enhances on-farm management and addresses consumer preferences for ethically produced and traceable meat, establishing a solid foundation for responsible and quality-focused production.

Case Study 2 Dairy Farm Automation in Wisconsin.

In dairy farming, the implementation of precision agriculture manifests in the form of automation and IoT technologies. A noteworthy example comes from a large dairy farm in Wisconsin, where IoT-enabled milking machines and sensors play a pivotal role in transforming traditional dairy operations.

The farm houses approximately 1,500 Holstein cows, a breed known for its high milk production. The IoT-enabled milking machines used on this farm are part of the DeLaval VMS V300 system, which is renowned for its efficiency and precision in milking operations. These milking machines go beyond simply automating the milking process. They also serve as continuous health monitors for dairy cows. The sensors integrated into these milking machines collect real-time milk production data including volume, fat content, and somatic cell count, which are crucial indicators of milk quality and cow health. Any irregularities, such as a drop in milk production or changes in cow behavior, trigger immediate alerts to the farm's management team. Machine learning algorithms are then employed to analyze this data further and predict potential disease outbreaks or health issues [120,121,122].

The proactive approach enabled by these technologies has substantially improved dairy farming. By intervening early, dairy farmers can provide timely treatment to cows in need, resulting in reduced veterinary costs and improved animal welfare. Additionally, the data collected is harnessed for optimizing feeding schedules, which benefits cow health and increases milk yields [119, 123, 124]. The farm reported about 20% increase in milk production and reduction in veterinary costs, and labor [125].

7.2 Precision horticulture

Case Study 3 Advancements in High-Tech Greenhouses in Netherlands.

Precision horticulture has seen a remarkable transformation in greenhouse farming in the Netherlands. Greenhouses have been equipped with a sophisticated array of IoT sensors, automated climate control systems, and AI-driven algorithms, ushering in a new era for cultivating crops like tomatoes and cucumbers.

A prominent case study comes from a state-of-the-art greenhouse facility in the Netherlands, where these technologies have been meticulously integrated. The facility encompasses 50 high-tech greenhouses, covering an area of approximately 10 hectares. IoT sensors operate round the clock, monitoring critical environmental parameters such as temperature, humidity, light intensity, and CO2 levels. AI algorithms process this data in real-time to create the optimal growing conditions for the crops. Automated systems adjust factors like ventilation, heating, and irrigation as required, providing the ideal environment for plant growth [99, 126, 127].

The type of equipment employed includes Priva climate control systems, which integrate with various IoT sensors to manage greenhouse conditions precisely. Advanced hydroponic systems are also used to optimize water and nutrient delivery to the plants, ensuring efficient resource use [127, 128].

The level of precision achieved through these technologies has had significant implications for greenhouse farming. Higher crop quality and increased yields have been realized. For instance, the average yield of tomatoes has increased by 25%, and the quality of cucumbers has shown a 20% improvement. Resource consumption, particularly water and energy, has been significantly reduced. About 30% reduction in water usage and a 20% decrease in energy consumption has been reported, making greenhouse farming more sustainable and profitable [129, 130]. Moreover, year-round production has become possible, ensuring a steady supply of fresh produce to meet market demands [126, 131].

Case Study 4 Vineyard Precision Farming in California’s Napa Valley.

In regions like California's Napa Valley, vineyards have embraced precision agriculture to enhance the quality of grapes, directly impacting the quality of the wine produced. Vineyard managers employ a combination of IoT sensors, drones, and AI models to monitor vine health, soil conditions, and weather data.

In a renowned Napa Valley vineyard, IoT soil sensors are embedded throughout the vineyard, continuously monitoring soil moisture levels. These sensors transmit data wirelessly to a centralized database accessible to vineyard managers through mobile apps. By analyzing this data, vineyard managers can precisely control irrigation, ensuring that vines receive the optimal amount of water [132].

The vineyard spans 120 hectares and primarily cultivates Cabernet Sauvignon and Chardonnay grape varieties. The precision farming techniques implemented include the use of Parrot Sequoia multispectral sensors and DJI Agras MG-1P drones for monitoring and maintaining vine health. Drones equipped with multispectral cameras fly over the vineyard, capturing high-resolution imagery. AI algorithms process this imagery to identify variations in vine health and detect issues such as water stress or pest infestations. This valuable information guides targeted interventions, including adjusting irrigation and applying pest control measures, resulting in premium wine production [96, 133].

The meticulous monitoring and data-driven decision-making contribute to producing high-quality wine and enhance the sustainability of vineyard practices. The vineyard reported about 15% increase in grape yield and a 20% improvement in grape quality metrics, including sugar content and acidity balance. Moreover, water usage was reduced by 25%, and pesticide application was optimized, reducing overall chemical usage by 18% [133,134,135].

7.3 Sustainable precision agriculture

Case Study 5 Advancements in Sustainable Rice Farming.

Rice cultivation is a cornerstone of global food production, but it is associated with substantial water consumption. In regions such as Southeast Asia, AI-driven irrigation systems have been implemented to optimize water usage in rice paddies.

In a large-scale project involving 150 rice fields across an area of approximately 500 hectares, IoT soil sensors and AI algorithms have been employed to monitor and manage water usage. These systems operate by continuously monitoring soil moisture levels and integrating weather forecasts. This data is used to precisely regulate irrigation, ensuring that rice paddies receive the right amount of water at the right time. The equipment used includes advanced soil moisture sensors, weather stations, and AI-based irrigation controllers. The result is reduced water wastage by 30% and enhanced rice yield by 20%, and 15% reduction in the incidence of water-related diseases in rice crops, further enhancing productivity and sustainability [45, 107, 136, 137].

Case Study 6 Precision Agriculture for Smallholder Farmers in Sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa face unique challenges such as limited access to resources, unpredictable weather patterns, and market uncertainties. However, precision agriculture technologies tailored to their needs offer promising solutions to overcome these obstacles.

In regions like Kenya and Ghana, initiatives are underway to deploy IoT-enabled soil sensors and low-cost drones on smallholder farms [138,139,140]. These technologies provide real-time data on soil moisture, nutrient levels, and crop health, empowering farmers to make informed decisions about irrigation scheduling, fertilizer application, and pest management.

Mobile-based applications, such as FarmConnect in Nigeria and M-Shamba in Kenya, are connecting smallholder farmers with agronomic advice and market information [141,142,143]. These apps offer personalized recommendations based on local conditions and allow farmers to access lucrative markets, thereby improving their profitability and market competitiveness.

In South Africa, precision agriculture technologies are also gaining traction, particularly in large-scale commercial farming operations. Companies like Aerobotics utilize drones and satellite imagery to monitor crop health and detect early signs of disease or pest infestations [144]. By providing actionable insights to farmers, these technologies enable proactive decision-making and yield optimization.

Similarly, in other sub-Saharan African countries such as Ethiopia, Tanzania, and Uganda, there is a growing interest in adopting precision agriculture practices. Organizations and government agencies are partnering with technology providers to introduce innovative solutions to smallholder farmers, ranging from weather forecasting tools to mobile payment systems for accessing inputs and markets.

Through the adoption of precision agriculture technologies, smallholder farmers in sub-Saharan Africa can achieve higher yields, reduce input costs, and improve their resilience to climate variability. Moreover, by increasing productivity and market access, these technologies have the potential to uplift rural livelihoods and contribute to poverty alleviation in agrarian communities.

These case studies underscore the tangible benefits of emerging technologies in precision agriculture. They exemplify how these innovations enhance efficiency, promote sustainability, and contribute to agricultural productivity and profitability. Precision agriculture has transcended the realm of theory; it is a reality reshaping the landscape of food production and livestock management.

8 Challenges and future directions

As precision agriculture continues to evolve and reshape the farming landscape, it faces various challenges that must be acknowledged and overcome to unlock its full potential. Simultaneously, promising future directions offer opportunities for further advancement, sustainability, and increased efficiency in agriculture.

8.1 Challenges

8.1.1 Data security and privacy concern

One of the foremost challenges in precision agriculture is the security and privacy of the vast data generated. This data encompasses crop yields, soil characteristics, and sensitive farm management strategies. Protecting this wealth of information from cyber threats and unauthorized access is paramount [20, 145,146,147] Farmers and agricultural technology providers must collaborate to ensure robust data encryption, secure cloud storage solutions, and comprehensive data access controls.

With the growing global market for precision agriculture, the amount of data processed and stored across cloud-based platforms will continue to expand, creating new vulnerabilities. Blockchain technology has been suggested as a future-proof solution to enhance data security by providing a decentralized and tamper-resistant system that can mitigate unauthorized access and data corruption. Industry-wide standards and best practices for data security are crucial for building trust and safeguarding sensitive data. Regular audits and assessments can help maintain the integrity of these systems and instill confidence in farmers regarding the security of their data [148,149,150]

8.1.2 Cost of implementation

The initial investment required for precision agriculture technologies, such as sensors, drones, and AI-driven analytics, can be a significant barrier for many farmers [15, 151]. This challenge is particularly pronounced for small-scale and resource-constrained farmers, especially in developing countries, where return on investments (ROI) may take up to 5 years due to higher initial costs and infrastructure limitations [151, 152].. In contrast, large-scale operations in developed economies often achieve ROI within 2–3 years, benefiting from economies of scale and government support.

As the global market for precision agriculture expands—projected to reach USD 24.09 billion by 2030 compound annual growth rate of 12.8% from 2024 to 2030 [153]—the costs associated with adopting precision agricultural technologies are expected to decrease. Economies of scale, technological advancements, and global competition among technology providers should drive prices down, making these tools more accessible to a broader range of farmers. In the meantime, government subsidies, grants, and alternative financing models, such as leasing and subscription services, will remain critical in bridging the affordability gap. Additionally, financial institutions and technology providers can offer innovative financing solutions, such as revolving credit lines tied to harvest cycles, further easing the financial burden on farmers.

8.1.3 Education and training

The effective utilization of emerging technologies in precision agriculture demands a skilled and knowledgeable farming workforce. The challenge here is to provide farmers with the necessary training to harness the full potential of precision agriculture. Educational programs should not only address the technical skills required (such as using sensors, drones, AI tools, etc.) but also the economic decision-making processes that help farmers optimize resource use and maximize ROI from their investments.

Bridging the knowledge gap is pivotal for reaping the benefits of these innovations[9, 154]. Comprehensive training programs should cover technology adoption, data-driven decision-making, and troubleshooting. Mobile applications and user-friendly interfaces for precision agriculture tools can simplify the learning curve for farmers, ensuring that they can effectively utilize these technologies. Collaborative initiatives between government agencies, educational institutions, and industry experts can help create training programs.

As the global market for precision agriculture continues to grow, scaling up educational programs is particularly important in emerging economies, where adoption is expected to expand rapidly. In these regions, tailored training that combines local agricultural practices with advanced technologies will be critical to unlocking the full potential of precision agriculture.

8.1.4 Regulatory and policy frameworks

Agricultural regulations and policies often lag behind the rapid evolution of technology in farming [155]. The integration of drones, IoT devices, and AI-driven decision-making systems in agriculture presents regulatory challenges. Without appropriate frameworks, there is a risk of regulatory gaps, misuse of technologies, and privacy violations concerning the vast amounts of data collected from farms.. Establishing comprehensive regulations that address these technologies is crucial for ensuring responsible and ethical integration into agricultural practices.

As the global market for precision agriculture expands, and more countries adopt these technologies, regulatory frameworks must adapt accordingly to keep up with this growth. Proactive policymaking is essential to create a balanced regulatory environment that supports innovation while safeguarding environmental and ethical concerns. Governments should also focus on supporting smallholder farmers through policies that incentivize the adoption of precision agricultural technologies while ensuring privacy protections are in place.

8.1.5 Environmental sustainability

While precision agriculture has the potential to enhance resource efficiency, its long-term environmental impact remains a subject of study and concern [156]. The challenge here is understanding and mitigating any adverse consequences and ensuring that precision agriculture practices are sustainable in the broader ecosystem. The future growth of the global precision agriculture market will depend on how well these environmental challenges are addressed. Technologies such as precision irrigation, which has been shown to reduce water consumption by at least 20%, offer a promising step forward. However, to fully capitalize on the environmental benefits of precision agriculture, greater emphasis must be placed on integrating these technologies with sustainable agricultural practices, including regenerative farming and biodiversity preservation. Adopting this comprehensive approach will not only enhance productivity but also safeguard long-term ecological balance, ensuring that precision agriculture contributes positively to both food security and environmental sustainability [106].

8.2 Future directions

8.2.1 Data security and privacy

As precision agriculture continues to evolve, ensuring the security and privacy of agricultural data remains a critical priority. With the increasing volume of data generated by farms, robust solutions such as data encryption, secure cloud storage, and clear data ownership policies will be essential for safeguarding sensitive information. Blockchain technology presents a promising solution by enabling decentralized and tamper-proof data storage, which enhances transparency and security across the agricultural data ecosystem [146, 147].

The rapid growth of the global precision agriculture market further underscores the need for comprehensive data security measures. Collaborations between technology providers, cybersecurity experts, and policymakers will be crucial in developing standardized protocols that address data privacy concerns while building trust among farmers and stakeholders. Regular audits and assessments will be necessary to ensure these standards are consistently upheld, providing farmers with confidence in the safety of their data.

Moreover, ongoing research into advanced cybersecurity measures, such as blockchain-based systems, could revolutionize data protection by offering enhanced security and transparency. By prioritizing these developments, the precision agriculture sector can mitigate risks and ensure that data remains a secure and valuable asset in the future of farming.

8.2.2 Affordability and accessibility

Efforts to make precision agriculture more accessible and affordable are critical to ensuring that a broader spectrum of farmers can benefit from these innovations. As the global market for precision agriculture expands, increased competition among technology providers and continuous advancements are expected to drive down the costs of adopting precision agriculture tools. These cost reductions will be particularly advantageous for smallholder farmers in developing regions, where financial constraints have historically slowed adoption.

Continued government support—through subsidies, grants, and tailored financing options—will play a pivotal role in facilitating access to precision agriculture technologies. Flexible leasing and subscription models are also expected to become more prominent, enabling farmers to adopt cutting-edge technologies without bearing the burden of large upfront costs. These financing mechanisms will not only help farmers maximize return on investment (ROI) but will also enhance the economic viability of precision agriculture on a larger scale.

Collaboration with technology companies can lead to the development of cost-effective solutions specifically tailored to the needs of small-scale and resource-constrained farmers. Moreover, financial institutions and agribusinesses can contribute by offering financing packages that align with agricultural production cycles. This approach ensures that farmers can invest in new technologies without straining their cash flow, making precision agriculture more accessible and sustainable for all types of farming operations.

8.2.3 Education and training

Future directions in education and training should focus on offering comprehensive programs that cover technology adoption, data-driven decision-making, and troubleshooting. Mobile applications and user-friendly interfaces for precision agriculture tools can simplify the learning curve for farmers. Collaborative initiatives between government agencies, educational institutions, and industry experts can help create training programs that are accessible and tailored to the needs of various farming communities. In addition, emerging technologies such as augmented reality (AR) and virtual reality (VR) can be leveraged to create immersive and interactive training experiences for farmers, facilitating better understanding and adoption of precision agriculture tools.

8.2.4 Regulatory and policy frameworks

Governments and regulatory bodies should collaborate with agricultural experts and technology providers to develop updated and flexible regulatory frameworks. These frameworks should balance innovation with environmental and ethical considerations, ensuring that emerging technologies benefit both farmers and the broader ecosystem. Proactive policymaking that accounts for the pace of technological change will help create a regulatory environment that supports responsible technology adoption in agriculture. Furthermore, governments can establish regulatory sandboxes to allow for controlled testing and deployment of emerging technologies, fostering innovation while ensuring regulatory compliance.

8.2.5 Environmental sustainability

Continued research into the environmental impact of precision agriculture is critical. Studies should evaluate factors such as soil health, water usage, and biodiversity. Innovations in sustainable farming practices, such as regenerative agriculture, can be integrated into precision agriculture to promote environmental stewardship. In addition to evaluating the environmental impact, research can focus on developing technologies and practices that actively contribute to environmental sustainability. For example, integrating IoT sensors to monitor soil health and biodiversity can help farmers make informed decisions that enhance sustainability. Collaboration between agricultural research institutions, environmental organizations, and precision agriculture stakeholders can drive the development of sustainable farming practices.

8.2.6 Integration and interoperability

Future directions in precision agriculture should emphasize the standardization of data formats and communication protocols. Open-source platforms that allow for seamless integration of diverse technologies can simplify the process for farmers. Industry collaboration and partnerships can drive these efforts, ensuring the technologies work together seamlessly. In addition, advancements in the development of Application Programming Interfaces (APIs) can further enhance interoperability between different precision agriculture tools, enabling farmers to choose the best solutions that suit their specific needs. Moreover, developing data marketplaces and platforms can facilitate the sharing and monetization of agricultural data, creating a more connected and efficient agricultural ecosystem.

8.2.7 Artificial intelligence advancements

Recent strides in artificial intelligence (AI) and machine learning present promising prospects for further revolutionizing precision agriculture [22, 26, 43]. Ongoing advancements focus on improving the capabilities of AI models in processing and interpreting agricultural data. Enhanced algorithms can contribute to more accurate predictions, enabling farmers to make informed decisions on resource allocation, pest control, and crop management. Additionally, the integration of AI with emerging technologies like quantum computing holds the potential to address complex agricultural challenges with unprecedented computational power, paving the way for more sophisticated and efficient farming practices.

8.2.8 Blockchain for data security

As precision agriculture relies heavily on data-driven insights, ensuring the security and traceability of this data is paramount [149]. Blockchain technology emerges as a potential solution to enhance data security in agriculture. By leveraging its decentralized and immutable nature, blockchain can safeguard sensitive agricultural information from unauthorized access and tampering. Moreover, blockchain's transparent and traceable characteristics offer a reliable system for documenting the journey of agricultural produce from farm to market. This enhances data security and establishes a trustworthy and accountable supply chain, addressing food safety and authenticity concerns. Exploring the application of blockchain in precision agriculture could contribute to the development of robust and secure data management systems, fostering greater trust among farmers and stakeholders alike.

In a nutshell, addressing the challenges and embracing future directions is pivotal for the success of precision agriculture. The synergy between overcoming obstacles and advancing solutions will shape a more efficient, sustainable, and resilient agricultural landscape. By leveraging technological innovations, comprehensive training, and forward-thinking policies, precision agriculture holds the potential to address the global challenge of feeding a growing population while safeguarding the environment. It represents a transformative force in modern farming that, when harnessed effectively, can result in a more prosperous and sustainable agricultural sector.

9 Conclusion

Precision agriculture stands at the forefront of agricultural innovation, offering transformative solutions to address the complex challenges facing modern farming. Through the integration of advanced technologies such as IoT, AI, and data analytics, precision agriculture enables farmers to make data-driven decisions, optimize resource management, and enhance productivity while minimizing environmental impact. As evidenced by the real-world case studies and ongoing research, precision agriculture has demonstrated its potential to revolutionize farming practices across diverse agricultural sectors and geographical regions.

As this sector continues to evolve, its global market—projected to reach USD 24.09 billion by 2030—presents both significant opportunities and notable challenges. While decreasing technology costs and improved accessibility are likely to foster broader adoption, issues such as data security, privacy, and the digital divide in rural areas remain pressing concerns, particularly in developing countries. Ensuring that these technologies are affordable and accessible to smallholder farmers will be crucial for promoting equitable adoption on a global scale.

The journey towards realizing the full potential of precision agriculture is not without its challenges. Data security, privacy, affordability, regulatory frameworks, and environmental sustainability all require careful consideration and proactive measures to ensure responsible and ethical implementation. Successfully integrating advanced technologies while managing risks like cyberattacks, data misuse, and rising costs will be pivotal in shaping the future of precision agriculture. Collaborative efforts among farmers, technology providers, governments, and research institutions, are essential to address these challenges and create an enabling environment for the widespread adoption of precision agriculture.

Looking ahead, the potential for precision agriculture is vast. Continued advancements, particularly in artificial intelligence, blockchain for data security, and precision robotics, will drive further innovation, making farming systems even more efficient and productive. Embracing these emerging technologies, along with enhancing education and training programs, strengthening regulatory frameworks, and promoting environmental stewardship, will be key drivers of progress in the field. By harnessing the power of precision agriculture, we can build a more resilient, sustainable, and prosperous agricultural sector that meets the needs of a growing global population while preserving our planet's resources for future generations.