1 Introduction

The ever-increasing global demand for animal products necessitates innovative solutions to improve animal welfare while maintaining economic viability for farmers. Animal welfare, once considered merely a moral imperative, has now come to be recognized as an essential element in sustainable agriculture. Its multidimensional nature encompasses the physical health, mental well-being, and natural behavior of livestock [1]. The welfare of animals has profound implications not only for ethical and social reasons but also for product quality, disease control, and environmental impact. Thus, improving animal welfare is a shared responsibility of farmers, technologists, policymakers, and consumers [2].

In the pursuit of superior animal welfare practices, objective indicators have emerged as indispensable tools for informed decision-making [3]. They provide a means to quantify and monitor welfare status, enabling early detection of potential issues, measuring the impact of interventions, and facilitating continuous improvement. Traditional assessment methods, relying on subjective evaluations or periodic physical examinations, can be labor-intensive, time-consuming, and prone to inconsistency [4]. The complexity of animal welfare, with its intricate interplay of physical, physiological, and behavioral factors, calls for more comprehensive and objective assessment methods.

This is where the role of sensor technology and artificial intelligence (AI) becomes paramount. In the age of digital transformation, these technologies offer compelling solutions to revolutionize livestock farming [5]. Sensor technologies, including wearable devices and environmental monitoring systems, can continuously capture a vast array of data points, such as body temperature, heart rate, movement patterns, feeding behavior, and environmental conditions. When coupled with AI algorithms, these data can provide real-time, objective, and holistic insights into animal welfare [6]. AI’s prowess in handling large data sets allows for precise and predictive analysis, making it possible to preempt health issues, optimize living conditions, and promote natural behaviors.

However, the successful deployment of these advanced technologies fundamentally depends on one crucial factor—the farmer. A farmer-centric approach is vital to ensure that technologies truly serve the needs of farmers and the welfare of animals [7, 8]. Farmers’ deep-rooted understanding of their animals and their environments must be the guiding force behind technological advancements. Technology should be a tool to enhance farmers’ capabilities, not to override them. A farmer-centric approach facilitates the design of intuitive, practical, and affordable solutions [9]. Moreover, it promotes technology acceptance and proper usage, ultimately leading to sustainable and effective animal welfare management.

To enhance the synergy between human expertise and AI, the farmer’s deep-rooted understanding of their livestock and surroundings must guide technological advancements. The transformative power of AI and sensor technologies in elevating animal welfare within livestock farming is evident. This paper delves into the nuances of objective animal welfare metrics, the importance of a farmer-centric approach, and the broader ethical and societal ramifications. By doing so, it bridges the gap between cutting-edge technological progress and its human stakeholders, offering a fresh and innovative viewpoint.

The integration of AI and sensor technologies into the agricultural sector is not just about the introduction of new tools but about a paradigm shift in how we approach farming. It's about merging the age-old wisdom of farmers with the cutting-edge advancements of the modern age. This synergy can lead to a more harmonious relationship between humans and animals, where technology aids in understanding and catering to the needs of livestock, rather than merely exploiting them for maximum yield.

Furthermore, the ethical considerations surrounding the use of AI in farming are paramount. As we move towards a more digitized world, questions about data privacy, security, and the ethical treatment of animals become more pressing. The data generated from these technologies is vast and can be used in various ways, both beneficial and potentially harmful. It's crucial to have robust guidelines and regulations in place to ensure that this data is used responsibly and ethically.

The exploration of sensor technologies and AI in livestock farming marks a critical juncture in the evolution of agriculture. As we stand on the brink of this exciting frontier, we must ensure that our journey is guided by a holistic vision, integrating scientific innovation, ethical responsibility, and social equity. This is the pathway to a future of livestock farming where technology, animals, and humans harmoniously coexist, fostering a symbiotic relationship that nurtures the welfare of all.

The digital transformation of the agricultural sector is not without its challenges. The rapid pace of technological advancement can sometimes outstrip the ability of regulatory bodies and ethical frameworks to keep up. This can lead to situations where technology is deployed without fully understanding its long-term implications. It’s essential to approach this transformation with a sense of caution and responsibility, ensuring that we do not lose sight of the fundamental principles of animal welfare and sustainable farming.

Moreover, the role of the farmer in this digital age cannot be overstated. While technology can provide tools and insights, it is the farmer who must make the final decisions about how to care for their livestock. The farmer’s deep understanding of their animals, honed over years of experience, cannot be replicated by any algorithm or sensor. It's essential to ensure that as we move forward, we do not lose this invaluable human touch.

In the end, the goal is to create a future where technology and humanity work hand in hand to create a more sustainable, ethical, and prosperous agricultural sector. A future where we harness the power of AI and sensors to enhance the welfare of animals, while also respecting the rights and needs of farmers and consumers. This is the vision that drives our research and the message we hope to convey through this review.

2 Animal Welfare Indicators: An In-Depth Exploration

In the domain of animal welfare, achieving a comprehensive understanding necessitates a multifaceted range of indicators encompassing the physical, physiological, and behavioral aspects of animals. These indicators are instrumental in gauging the overall well-being of animals and serve as the bedrock for informed decision-making and intervention [10].

2.1 Understanding the Comprehensive Array of Indicators

2.1.1 Health Indicators: A Window into Physical Well-Being

Health indicators serve as the most direct and immediate measures of an animal's welfare, manifesting as tangible signs such as the presence of diseases, injuries, or alterations in physical condition [11,12,13]. These indicators can be assessed through clinical examinations by veterinarians, laboratory tests, or visual inspections. Their sensitivity lies in their ability to detect both glaring health issues and subtle health shifts. For instance, normal body condition serves as a positive health indicator, whereas the presence of diseases or injuries represents negative indicators. Stable body weight is an example of a neutral health indicator (Table 1).

Table 1 Comprehensive classification of animal welfare indicator categories: nature, measurement, sensitivity, and examples
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2.1.2 Behavioral Indicators: Unveiling Mental and Emotional States

Behavioral indicators offer insights into the mental and emotional facets of animal welfare, providing a glimpse into an animal's inner world and reflecting its comfort, stress, or frustration levels [14, 15]. These indicators encompass a broad spectrum of behaviors, ranging from eating and sleeping patterns to social interactions and the expression of natural behaviors. Continuous observations, ethological studies, and advanced techniques like video analysis and machine learning are employed to monitor behavioral patterns. Behavioral indicators are highly sensitive, capable of capturing subtle and nuanced changes in behavior. Positive behavioral indicators may include active social interactions and engagement in natural behaviors, while negative ones could involve reduced social interactions or increased aggression. Routine feeding behavior can be categorized as a neutral indicator.

2.1.3 Physiological Indicators: Deciphering the Language of the Body

Physiological indicators, operating beneath the surface like the hidden depths of an iceberg, offer objective assessments of an animal's physiological responses to its environment [16, 17]. These indicators can be continuously monitored using wearable sensors, laboratory tests, or advanced continuous monitoring systems. They provide real-time insights into an animal’s physiological state and can detect early responses to stressors. Positive physiological indicators may encompass a stable heart rate and normal body temperature. Conversely, negative indicators may involve elevated stress hormone levels or an increased heart rate. Baseline physiological parameters are examples of neutral indicators.

2.1.4 Interplay of Animal Welfare Indicators: A Comprehensive Understanding

Understanding the intricate interplay among these vital animal welfare indicators is essential for a nuanced assessment of animal well-being. These indicators, encompassing health, behavior, and physiology, provide multidimensional insights into the lives of animals, enabling us to address their needs and improve their quality of life.

2.1.5 A Compassionate Approach to Animal Welfare Indicators

In conclusion, animal welfare indicators offer a multifaceted lens through which we can assess the well-being of animals. Their nature, sensitivity, and subtlety make them invaluable tools for evaluating and enhancing animal welfare. By recognizing these indicators as interconnected components of a holistic system, rather than isolated data points, we can adopt a more compassionate and precise approach to caring for animals.

As technology continues to advance, particularly through the integration of sensor technology and artificial intelligence, our ability to monitor and respond to these indicators becomes increasingly sophisticated. Subsequent sections of this paper delve into how these technological advancements empower us to revolutionize the assessment and improvement of animal welfare, placing the well-being of animals at the forefront of modern agriculture.

2.2 Current Practices and Their Limitations

Current practices for assessing animal welfare primarily rely on manual, periodic inspections and evaluations. While these methods can provide valuable insights, they come with several limitations.

Health indicators are generally assessed through visual inspections and veterinary examinations. However, these methods can be labor-intensive, time-consuming, and potentially stressful for the animals. More significantly, periodic examinations may miss sudden health issues or subtle, gradual changes in an animal’s condition.

Behavioral indicators are typically evaluated by observing the animals over time. This approach requires a keen understanding of animal behavior and can be subjective, varying based on the observer’s knowledge and interpretation. Moreover, continuous observation is impractical in large-scale farming operations.

Physiological indicators are often measured using specialized equipment and laboratory testing, making them costly and impractical for regular monitoring. Furthermore, capturing these measures can be invasive and stressful for the animals.

2.3 Potential of Sensor Technology and AI for Objective, Real-Time Assessment

The advent of sensor technology and AI offers promising solutions to overcome these limitations, enabling real-time, objective, and non-invasive assessment of animal welfare indicators.

Sensor technologies, such as wearable devices, can continuously monitor health and physiological parameters like body temperature, heart rate, and movement. Environmental sensors can detect changes in the animal’s surroundings that may affect its wellbeing. Automated video analysis can observe and interpret animal behavior on a scale far beyond human capabilities [18].

Coupling these sensor data with AI algorithms can unlock new levels of understanding and monitoring of animal welfare. AI can analyze vast amounts of data, identify patterns, and make predictions [19]. For instance, it can learn to detect subtle changes in behavior that indicate stress or illness, often before visible symptoms appear. AI algorithms can also integrate data from multiple sources, providing a holistic picture of an animal’s welfare.

Moreover, AI-driven systems can alert farmers to potential issues in real time, enabling swift interventions. By predicting potential health issues or welfare risks, these systems can support proactive rather than reactive management, potentially preventing problems before they occur.

In essence, the combined potential of sensor technology and AI promises a revolution in animal welfare assessment. However, to achieve this potential, we must ensure that these technologies are implemented in a manner that respects and supports the crucial role of farmers in animal welfare management. With the farmer-centric approach at its core, this digital transformation can truly enhance animal welfare and sustainable farming practices.

2.4 Sensor Technologies in Livestock Farming: A Transformative Tool for Animal Welfare

Sensor technologies have emerged as a significant contributor to the evolution of precision livestock farming, opening up a wealth of opportunities for enhanced animal welfare and efficient farm management. By providing continuous, real-time data on various health, behavioral, and environmental parameters, these technologies enable more informed, timely, and accurate decision-making [5].

2.4.1 Types of Sensors Technologies

The burgeoning landscape of sensor technologies in livestock farming encompasses an array of tools, each offering unique capabilities. They can be broadly categorized into wearable sensors, environmental sensors, and imaging technologies.

Wearable Sensors: As the name suggests, these devices are attached directly to the animals, often in the form of collars, ear tags, or implants. They are designed to monitor various physiological parameters such as body temperature, heart rate, and movement [20]. For example, accelerometers can capture detailed movement data, providing insights into the animals’ activity levels, feeding behavior, or signs of restlessness. Similarly, rumination sensors can track an animal’s chewing activity, offering valuable information about its digestive health and welfare.

Environmental Sensors: These sensors are used to monitor environmental conditions that can significantly impact animal welfare. This includes ambient temperature, humidity, air quality, light intensity, and noise levels. By providing real-time feedback on the environment, these sensors can help maintain optimal living conditions for the animals and identify any adverse changes [21] promptly.

Imaging Technologies: Advanced imaging technologies such as CCTV cameras, thermal cameras, and 3D imaging systems can capture a wealth of information about animal behavior and physical condition. Combined with computer vision and machine learning algorithms [22], these systems can analyze animal movements, social interactions, body condition, and even detect physical abnormalities.

2.4.2 Strengths and Weaknesses of Sensor Technologies

Sensor technologies offer several key advantages in the context of animal welfare. By providing continuous, real-time data, they allow for proactive management and early detection of potential issues. Moreover, they offer objective, quantifiable measures of welfare, reducing reliance on subjective assessments.

However, sensor technologies are not without their limitations. Wearable sensors, for instance, need to be robust, lightweight, and comfortable for the animal, which can pose design challenges. They may also require regular maintenance or battery replacements, adding to the workload and costs.

Environmental sensors, while highly useful, can only provide indirect measures of animal welfare, and may not accurately reflect individual animals’ experiences within a herd.

Imaging technologies, despite their advanced capabilities, can be expensive and require substantial computational resources. They may also struggle with issues like poor lighting, occlusions, or distinguishing between animals in crowded conditions.

2.4.3 Current Research and Applications in Livestock Farming

The application of sensor technologies in livestock farming is a dynamic and rapidly advancing field. Wearable sensors are becoming increasingly common in dairy farming, where they are used to monitor cows’ health, estrus cycles, and feeding behavior. These systems can alert farmers to potential health issues or when a cow is ready to be inseminated, improving herd health and productivity.

Environmental sensors are widely used in poultry farming to maintain optimal temperature, humidity, and ventilation in chicken houses. Recent research is exploring the potential of these sensors for detecting harmful gases like ammonia, which can adversely affect the birds' health and welfare.

Imaging technologies are being harnessed for diverse applications, from monitoring pig behavior and detecting lameness in cows, to automated body condition scoring in dairy cattle. Thermal imaging, in particular, is showing promise for non-invasive detection of health issues like inflammation or stress.

Sensor technologies hold immense potential for enhancing animal welfare in livestock farming. However, their successful deployment requires careful consideration of their strengths, limitations, and the specific needs and conditions of each farm. Furthermore, it is crucial to remember that these technologies are tools to aid, not replace, the farmers' expertise and care. By integrating sensor technologies within a farmer-centric approach, we can harness their full potential to create a more sustainable, humane, and efficient livestock industry.

3 AI Algorithms for Animal Welfare Assessment: Innovating Livestock Farming

Artificial intelligence (AI) has emerged as a transformative tool for data-driven decision-making in various fields, and livestock farming is no exception. The deluge of sensor data generated on farms is a gold mine of insights, but extracting these insights requires advanced computational techniques. AI algorithms can analyze these complex, multi-dimensional data, making sense of patterns [23] and trends that may be too subtle or complex for the human eye. These capabilities unlock unprecedented possibilities for real-time, predictive, and holistic assessment of animal welfare.

3.1 AI Techniques for Sensor Data Analysis

AI brings a toolkit of sophisticated techniques for analyzing sensor data, each with its unique strengths and applications. Two of the most relevant techniques for animal welfare assessment are machine learning and predictive modeling.

Machine Learning: Machine learning algorithms learn from data, identifying patterns and making predictions or decisions without being explicitly programmed. They can analyze large and complex datasets, uncovering relationships that may not be readily apparent. Supervised learning algorithms, such as decision trees, support vector machines, and neural networks, can be trained to classify animals based on their welfare status, such as healthy vs. diseased or stressed vs. comfortable. Unsupervised learning algorithms, like clustering techniques, can group animals based on similarities in their sensor data, potentially revealing different welfare needs within a herd.

Predictive Modeling: Predictive modeling involves using statistical techniques to predict future outcomes based on historical data. In the context of animal welfare, predictive models can forecast potential health issues or welfare risks, allowing for proactive interventions [24, 25]. For instance, a model could predict the risk of lameness based on a cow’s movement data or forecast a disease outbreak based on trends in body temperature and eating behavior.

3.2 Current Research and Applications in Animal Welfare Assessment

The application of AI in animal welfare assessment is a rapidly growing field, with promising results in both research and practical applications. Here are a few examples:

Health Monitoring: Machine learning algorithms have been successfully trained to detect diseases in livestock based on sensor data. For example, research has demonstrated the ability of AI to identify respiratory diseases in pigs based on sound data, and to detect mastitis in dairy cows based on milk conductivity measurements. These systems can alert farmers to health issues in real-time, enabling early treatment and reducing the spread of disease.

Behavior Analysis: AI can analyze complex behavioral data, providing insights into animals’ mental welfare. For instance, machine learning algorithms have been used to classify pig behaviors from video data, identifying behaviors associated with comfort or stress. This information can help farmers optimize living conditions and manage social dynamics within a herd.

Predictive Welfare Assessment: Predictive models are being developed to forecast welfare issues before they occur. For example, a study used machine learning to predict the risk of lameness in dairy cows based on accelerometry data, with over 90% accuracy. These tools can support preventive healthcare and welfare management, reducing the prevalence of welfare issues and improving animals’ quality of life.

Decision Support Systems: AI can integrate and analyze multiple sources of data, providing holistic assessments of animal welfare and personalized recommendations for management. Decision support systems can use AI to interpret sensor data, evaluate welfare status, predict future risks, and suggest interventions [26]. This can guide farmers in making informed, timely, and effective decisions, enhancing both productivity and animal welfare.

AI algorithms hold immense promise for advancing animal welfare in livestock farming. By making sense of sensor data, they can provide real-time, predictive, and comprehensive insights into animals’ wellbeing. However, the successful deployment of AI in farming requires careful consideration of its limitations, ethical implications, and the context-specific needs of each farm. Most importantly, it must be integrated within a farmer-centric approach, complementing the farmers’ expertise and serving their needs. By doing so, we can harness the full potential of AI to create a more sustainable, humane, and efficient livestock industry.

The convergence of AI and sensor technologies has brought forth the imperative for a systematic and comprehensive framework. This is eloquently represented by the Algorithmic Life Cycle in Human-Centric Animal Welfare Management, as illustrated in Fig. 1. This life cycle delineates the intricate journey of AI-driven solutions from their inception to their practical deployment. Beginning with the meticulous collection of data, it progresses to the design of bespoke algorithms tailored to the unique challenges of animal welfare. Subsequent stages involve rigorous validation, ensuring that these algorithms not only function optimally but also resonate with the core values of animal welfare. The life cycle culminates in the real-world application, where the technology interfaces directly with the dynamic environments of livestock farming. Throughout this continuum, there’s an unwavering emphasis on human-centric principles. These principles champion the well-being of animals, leverage the invaluable insights of farmers, and navigate the myriad ethical quandaries associated with integrating advanced technologies into traditional farming practices. By meticulously adhering to this life cycle, the promise is a future where livestock farming harnesses cutting-edge technology, yet remains deeply rooted in ethical considerations, ensuring a symbiotic relationship between innovation, animal welfare, and human expertise.

Fig. 1
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Algorithmic life cycle in human-centric animal welfare management

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In our exploration of the integration of AI and sensor technologies within livestock farming, we've identified several pivotal elements that emphasize a human-centric approach. These elements not only highlight the technological advancements but also underscore the profound impact on animal welfare and the deepening bond between farmers and their livestock. A comprehensive overview of these elements and their implications from both a livestock farming and human-centric perspective is shown in Table 2.

Table 2 Deep dive: pillars of AI and sensor technologies in human and farmer-centric digital livestock farming
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4 Farmer-Centric Approach to Technology Adoption: Catalyzing Change in Livestock Farming

The escalating pace of technological advancement in agriculture has brought an array of sophisticated tools to farmers’ fingertips. Yet, the mere existence of these technologies does not guarantee their effective utilization. A pivotal ingredient to the recipe for successful technology adoption in agriculture is understanding the farmer’s perspective [27, 28], often encapsulated in a farmer-centric approach. This approach posits the farmer as the primary stakeholder, recognizing their unique knowledge, needs, and challenges, and integrating these elements into the technology development and implementation process.

4.1 Understanding the Farmer-Centric Approach

At its core, a farmer-centric approach values the insights and experiences of farmers, acknowledging that their intimate understanding of their animals and their environments is unmatched by any technology. It emphasizes the importance of developing technologies that address the real-world needs of farmers and enhancing their capabilities rather than attempting to replace them [29].

A farmer-centric approach is not about delivering ready-made solutions to farmers. Instead, it involves farmers in the technology development process, using their feedback to shape the design and functionality of technologies. It seeks to develop solutions that are practical, affordable, and easy-to-use in the context of a farm, recognizing that the value of a technology lies in its utility to the farmer.

4.2 Factors Influencing Farmers’ Technology Adoption Behaviors

Despite the obvious benefits of advanced technologies, their adoption in farming is not always straightforward. A multitude of factors influence farmers’ technology adoption behaviors. These can broadly be categorized into personal factors, farm-related factors, and technology-related factors.

Personal Factors: Farmers’ attitudes, knowledge, and skills significantly influence their willingness to adopt new technologies. Age, education, and previous experience with technology can also play a role. Furthermore, the farmer’s values and beliefs about animal welfare, sustainability, and traditional farming methods can impact their openness to technological innovation.

Farm-Related Factors: The characteristics of a farm can shape the feasibility and attractiveness of technology adoption. The farm’s size, type of livestock, financial resources, and labor availability can all impact the decision to adopt a technology. Additionally, the geographical location of the farm can influence access to technology and related support services.

Technology-Related Factors: The characteristics of the technology itself are critical to its acceptance. The perceived usefulness and ease of use are key determinants of technology adoption. The cost of the technology, as well as the expected return on investment, can also influence the decision. Furthermore, the compatibility of the technology with existing practices, equipment, and infrastructure can affect its adoption.

4.3 Strategies for Promoting Technology Adoption Among Farmers

Given the complex interplay of factors influencing technology adoption, a multifaceted strategy is required to promote the uptake of technologies among farmers.

User-Centric Technology Design: By involving farmers in the technology design process, we can ensure that the end product is tailored to their needs, abilities, and contexts. User testing and feedback should be integral to technology development, ensuring practical, intuitive, and robust solutions.

Education and Training: Providing farmers with the knowledge and skills to use new technologies is crucial for successful adoption. This could involve hands-on training sessions, instructional videos, or user-friendly manuals. Furthermore, ongoing technical support should be available to assist farmers with any issues or questions.

Demonstrating Value: Farmers need to see the tangible benefits of a technology to invest in it. This could involve demonstrating how the technology can improve animal welfare, increase productivity, or save time and resources. Case studies, on-farm trials, or economic analyses can provide compelling evidence of a technology’s value.

Facilitating Access: Ensuring that farmers have access to technologies and related services is critical for adoption. This could involve strategies to reduce the cost of technologies, such as subsidies or financing options. Additionally, remote farmers should have access to support services, either digitally or through mobile services.

A farmer-centric approach is not merely a noble ideal but a pragmatic strategy for successful technology adoption in livestock farming. It recognizes the pivotal role of farmers in animal welfare management and harnesses their wisdom to shape technological innovations. By integrating a farmer-centric approach into the development and deployment of sensor technology and AI in livestock farming, we can catalyze a shift towards a more sustainable, humane, and efficient industry. Through this collaborative endeavor, we can create a future where technology serves farmers and animals alike, enhancing the welfare of livestock and the livelihoods of those who care for them.

To shed light on the complex relationship between human-centric principles and intelligent systems within the domain of animal farming, Fig. 2 serves as a pivotal visual guide. This figure intricately maps out the fusion of cutting-edge technology with foundational ethical considerations, emphasizing the paramount importance of human values in the equation. By doing so, it paints a comprehensive picture of a harmonized ecosystem where technological advancements work hand-in-hand with human ethics. This synergy ensures an environment that is not only technologically advanced but also deeply considerate of the welfare and well-being of livestock. Furthermore, it underscores the potential for farmers to harness these technologies in ways that resonate with their values, fostering a more humane and efficient farming landscape.

Fig. 2
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Comprehensive human-centric intelligent ecosystem in animal farming

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5 Navigating the Ethical and Social Terrain of Technology Adoption in Livestock Farming

As sensor technology and AI find increasing application in livestock farming, it is essential to address the ethical and social considerations that accompany this digital transformation [30]. While these technologies offer significant potential for enhancing animal welfare, they also raise new questions about animal privacy, data security, and the social implications of technology adoption. Carefully navigating these concerns is crucial to ensure a responsible and equitable transition towards digital farming.

6 Ethical Issues in Using Sensors and AI for Animal Welfare

6.1 Animal Privacy

The concept of privacy, traditionally a human-centric concern, has found new relevance in the realm of animal farming with the advent of sensor technologies [31]. Continuous monitoring of animals, while serving the commendable purpose of enhancing welfare, raises complex ethical questions. Animal privacy emerges as a novel yet vital consideration in this context. Wearable sensors and continuous surveillance, though beneficial for health monitoring, might impact animals’ natural behaviors, cause discomfort, or disrupt social dynamics. Unlike human subjects, animals cannot consent to being monitored, placing the responsibility on farmers, technologists, and policymakers to define acceptable boundaries. Striking a balance between necessary observation and intrusive surveillance is paramount. Ethical guidelines must be crafted to ensure that monitoring prioritizes the welfare of the animals [32], avoiding unnecessary intrusion into their natural behaviors and habitats.

6.2 Data Security

With the integration of sensor technologies and AI in modern farming, a deluge of sensitive data is generated, encompassing genetic information, health records, and behavioral patterns. This technological advancement not only heralds a new era of precision and efficiency but also brings to the fore critical concerns about data security and ownership. The potential risk of data breaches looms large, with far-reaching implications for a farm’s reputation and competitive standing [33]. Moreover, the question of ownership and permissible use of the data necessitates clear and unequivocal guidelines. Implementing robust data security measures becomes paramount in this landscape. Stringent protection protocols, adherence to relevant regulations, and a cultivated culture of data stewardship are essential in safeguarding the integrity and confidentiality of animal-related information. These measures collectively ensure that the promise of technology does not overshadow the ethical imperatives of privacy and responsible data management in the complex ecosystem of animal farming.

6.3 Fostering Transparency and Accountability with Explainable AI

Explainable AI (XAI) stands as a transformative pillar in the landscape of artificial intelligence. It champions the imperative for AI models to provide not only accurate predictions but also clear and comprehensible explanations for their decisions and actions. In an era where AI-driven systems are increasingly deployed in various domains, its significance in ensuring transparency, accountability, and ethical AI practices cannot be overstated.

In the context of animal farming, where AI-driven decisions exert a profound influence on animal well-being and farm management, the demand for transparency and interpretability reaches new heights. Farmers, veterinarians, and an array of stakeholders within the agricultural ecosystem must possess the capacity to understand how and why specific determinations are made by AI systems. This comprehension goes beyond mere curiosity; it is the bedrock upon which trust is built, forming the linchpin of effective collaboration between human experts and AI entities. Furthermore, it serves as a safeguard to ensure that AI decisions align with ethical tenets and welfare standards [34, 35].

The journey toward achieving AI models that seamlessly integrate robust predictive capabilities with transparency is far from a solitary endeavor. Instead, it necessitates a unified and interdisciplinary effort involving researchers, technologists, and industry practitioners. Collaboration across these domains is paramount, as it bridges the chasm between the computational prowess of AI and the imperative need for accountable, intelligible AI systems in animal farming.

Recent international conferences on Explainable AI (XAI) have provided a platform for researchers and practitioners to exchange ideas and present innovative approaches aimed at enhancing the interpretability of AI models [36, 37]. These conferences have served as crucibles of knowledge, incubating new methodologies and frameworks that hold the promise of revolutionizing the transparency and explainability of AI systems. Importantly, they have recognized and emphasized the critical role of XAI in diverse applications, ranging from healthcare to finance and, indeed, agriculture.

One of the key takeaways from these conferences is the recognition that XAI is not a one-size-fits-all solution but rather a spectrum of techniques and methods tailored to specific contexts and user needs. In the realm of animal farming, this means customizing XAI solutions to align with the unique challenges and intricacies of the industry. For instance, XAI techniques in healthcare may differ significantly from those applied in agriculture due to variations in data types, decision-making processes, and ethical considerations.

One promising avenue explored in recent XAI research is the development of interpretable machine learning models that inherently provide clear rationales for their predictions. These models are designed with transparency as a foundational principle, making it easier to understand their decision-making logic. In the context of animal farming, this could mean adopting machine learning techniques that not only deliver accurate results but also reveal the key factors and features that led to a specific decision. For example, in disease diagnosis, an interpretable AI model can elucidate which symptoms, biomarkers, or historical data points contributed most significantly to the diagnosis.

Another noteworthy development in the XAI landscape is the fusion of AI and domain-specific knowledge. In animal farming, where domain expertise is invaluable, AI systems can be enhanced by incorporating insights from veterinarians, animal scientists, and farmers themselves. These experts can collaborate with AI researchers to create models that not only make predictions but also provide contextual explanations rooted in the realities of farm management and animal care.

Furthermore, the importance of human-AI collaboration is accentuated by recent research efforts that focus on the concept of “glass box” AI models. Unlike the traditional “black box” models, which offer little insight into their internal workings, glass box models are designed to be transparent and easily interpretable. This transparency empowers users to validate AI decisions, identify potential biases, and ensure that ethical and welfare considerations are upheld.

XAI is an indispensable asset in the realm of animal farming, offering a pathway to transparency, accountability, and the ethical use of AI technologies. As the field of XAI continues to evolve, it holds the promise of not only enhancing the performance of AI systems but also fostering trust and ethical practices in the intricate ecosystem of animal farming.

Some XAI models or systems that can possibly be applied to animal welfare and farming animals:

Concept Relevance Propagation (CRP): CRP is an XAI approach that can provide insights into AI models' decision-making processes. It can be applied to understand and interpret AI systems used in animal welfare applications.

Local Interpretable Model-agnostic Explanations (LIME): LIME is a framework that can be used to explain the predictions of machine learning models, including those used in animal farming and welfare. It can help make black-box models more understandable.

SHapley Additive exPlanations (SHAP): SHAP is an XAI framework that provides explanations for individual predictions. It can be applied to AI models used in animal welfare to understand the importance of different features in making decisions.

Interpretable Temporal Convolutional Networks (iTCN): iTCN is an interpretable deep learning model that can be used in applications involving time-series data, such as monitoring animal behavior on farms.

Gradient-weighted Class Activation Mapping (Grad-CAM): Grad-CAM is a technique that visualizes the regions of an image that are important for a deep learning model’s prediction. It can be applied to understand how AI models analyze images related to animals on farms.

SHAPLEY feature selection for Bayesian Neural Networks (SHAPLEY-BNN): This method combines SHAP values with Bayesian Neural Networks and can be used to explain and interpret the decisions made by AI models in animal farming.

Counterfactual Explanations: Counterfactual explanations involve providing alternative scenarios to explain AI model predictions. They can be used to understand how changing inputs or conditions might impact animal welfare predictions.

Explainable Reinforcement Learning (XRL): XRL techniques can be applied to reinforcement learning models used in managing and optimizing animal farming processes. They help in understanding the policies and decisions made by AI agents.

Integrated Gradients: Integrated Gradients is an attribution method that assigns importance scores to input features. It can be used to explain the role of various factors in AI models used for animal welfare assessment.

Human-AI Collaboration Tools: Some XAI systems are designed to facilitate collaboration between human experts and AI models. These tools can be applied in scenarios where experts work together with AI systems to improve animal welfare practices.

These XAI models and techniques can contribute to greater transparency, accountability, and ethical decision-making in the context of animal farming and welfare. They enable stakeholders to better understand and trust the decisions made by AI systems while ensuring the well-being of animals.

6.4 Responsible AI: Ensuring Ethical, Transparent, and Sustainable AI in Animal Farming

In the rapidly evolving landscape of artificial intelligence, the issue of algorithmic opacity, often referred to as the “black box” problem, has garnered significant attention and concern. The inherent lack of transparency in AI algorithms can impede our ability to comprehend and trust the recommendations and decisions they generate, particularly when these algorithms are trained on biased or incomplete datasets. This opacity poses substantial challenges, especially in domains where AI-driven systems have a substantial impact, such as animal farming.

In the context of animal farming, achieving responsible AI practices extends beyond mere legal compliance [2]. It involves a profound commitment to upholding ethical principles, respecting social values, and promoting sustainability. Responsible AI in animal farming encompasses the deployment of technologies that not only enhance animal welfare but also support and bolster sustainable agricultural practices while remaining sensitive to the values held by local communities. This commitment necessitates an ongoing process of ethical evaluation, continuous stakeholder engagement, and adaptability to emerging insights and evolving societal expectations.

Responsible AI is not a solitary endeavor but a multidisciplinary and collaborative one. It requires the concerted efforts of ethicists, animal welfare experts, farmers, and technologists. Together, they work toward ensuring that AI serves the greater good without compromising ethical integrity. In essence, responsible AI becomes more than just a technological pursuit; it evolves into a holistic endeavor that harmonizes innovation with ethical stewardship within the intricate landscape of modern animal farming.

6.4.1 The “Black Box” Predicament and Its Implications

The “black box” problem in AI refers to the inherent opacity of many machine learning algorithms, particularly deep learning models. These algorithms, while capable of making accurate predictions and decisions, often do so without providing a clear and interpretable rationale for their choices. This opacity can be problematic for several reasons, especially in applications where the consequences of AI decisions are substantial.

In animal farming, where AI systems are increasingly being employed to optimize various aspects of farm management, the “black box” problem raises several concerns:

  1. 1.

    Lack of Accountability:

    When AI systems make decisions without offering transparent explanations, it becomes challenging to attribute responsibility for those decisions. In cases of undesirable outcomes or ethical dilemmas, identifying who or what is accountable can be elusive. Responsible AI practices aim to address this issue by introducing transparency and traceability into AI decision-making processes.

  2. 2.

    Trust and Adoption:

    Farmers, veterinarians, and other stakeholders in animal farming need to have confidence in AI-driven recommendations and decisions. Trust is a cornerstone of successful AI adoption, and it can be eroded when users cannot understand or trust the reasoning behind AI-generated outcomes. Responsible AI seeks to rebuild and reinforce this trust by providing clear explanations for AI decisions.

  3. 3.

    Ethical Considerations:

    AI algorithms are only as unbiased as the data they are trained on. In animal farming, biased data can lead to AI decisions that inadvertently discriminate against certain animals or favor specific practices. Responsible AI acknowledges the ethical dimension of AI applications and strives to mitigate bias while promoting fairness and ethical treatment of animals.

6.4.2 Responsible AI in Animal Farming: A Holistic Approach

Responsible AI in the context of animal farming extends beyond mere compliance with legal regulations. It represents a comprehensive commitment to ethical, transparent, and sustainable AI practices. This commitment manifests in several key principles and practices:

  1. 1.

    Ethical Assessment:

    Ethical considerations are at the forefront of responsible AI in animal farming. Ethicists, in collaboration with domain experts, help identify potential ethical challenges and dilemmas posed by AI applications. They assess the impact of AI decisions on animal welfare, human stakeholders, and the broader environment. Ethical assessments provide a foundation for designing AI systems that align with ethical principles.

  2. 2.

    Stakeholder Engagement:

    Responsible AI recognizes the importance of engaging with various stakeholders, including farmers, veterinarians, local communities, and consumers. These stakeholders offer diverse perspectives and values that must be considered in AI system design and deployment. Engaging with stakeholders ensures that AI solutions are sensitive to the needs and values of the communities they serve.

  3. 3.

    Transparency and Interpretability:

    Addressing the “black box” problem requires a concerted effort to enhance the transparency and interpretability of AI models. This can be achieved through the development of interpretable machine learning models that provide clear rationales for their decisions. In animal farming, transparent AI models can elucidate how specific factors, such as animal health indicators or environmental conditions, influence AI-generated recommendations.

  4. 4.

    Bias Mitigation:

    Biased data can perpetuate inequalities and unfair treatment in AI-driven decision-making. Responsible AI employs techniques to identify and mitigate bias in training data and algorithms. This includes actively seeking diverse and representative datasets and implementing fairness-aware machine learning methods.

  5. 5.

    Sustainability:

    Sustainable agricultural practices are a fundamental consideration in responsible AI for animal farming. AI solutions should not only optimize efficiency but also promote practices that are environmentally sustainable and respectful of natural resources. Sustainability assessments are integrated into AI system evaluations.

  6. 6.

    Adaptability and Continuous Improvement:

    The landscape of animal farming is dynamic, with evolving insights, changing societal expectations, and emerging challenges. Responsible AI is adaptable and embraces a culture of continuous improvement. AI systems are designed to evolve with new information and feedback from stakeholders.

6.4.3 The Multidisciplinary Collaboration

Responsible AI in animal farming necessitates a collaborative effort that brings together experts from various domains:

  1. 1.

    Ethicists:

    Ethicists specializing in AI ethics play a crucial role in guiding the ethical assessment of AI applications in animal farming. They ensure that AI systems align with moral principles and ethical frameworks.

  2. 2.

    Animal Welfare Experts:

    Experts in animal welfare contribute their knowledge to assess the impact of AI decisions on the well-being of animals. They advocate for practices that prioritize the health and humane treatment of animals.

  3. 3.

    Farmers:

    Farmers are vital stakeholders in responsible AI. Their practical experience and insights inform the development and deployment of AI solutions that address real-world challenges in farming.

  4. 4.

    Technologists:

    AI technologists and researchers work on the technical aspects of responsible AI, including the development of interpretable models, bias mitigation techniques, and sustainability assessments.

6.4.4 Responsible AI in Action

To illustrate the principles of responsible AI in animal farming, let's consider a hypothetical scenario: the use of AI-based systems to monitor and optimize the health and productivity of dairy cows on a farm.

Ethical Assessment: Ethicists collaborate with veterinarians to assess the ethical implications of using AI to monitor dairy cows. They identify potential concerns related to animal ‘privacy’, data security, and the humane treatment of cows.

Stakeholder Engagement: Farmers and local communities are actively engaged in the AI deployment process. Their input helps shape the design of the AI system. For example, farmers may emphasize the importance of minimizing stress on cows during data collection.

Transparency and Interpretability: AI models used for cow monitoring are designed to be transparent. When the AI system recommends specific interventions, it provides clear explanations based on factors such as cow behavior, vital signs, and historical health data.

Bias Mitigation: The AI system is trained on a diverse dataset representing various cow breeds and farm conditions. Bias mitigation techniques are applied to ensure that the AI system does not favor specific groups of cows unfairly.

Sustainability: The AI system not only focuses on maximizing milk production but also considers environmental sustainability. It recommends practices that reduce water usage and minimize the farm’s carbon footprint.

Adaptability and Continuous Improvement: The AI system evolves over time as new research findings on dairy cow health emerge. Feedback from farmers and veterinarians is incorporated to enhance the system’s accuracy and effectiveness.

Responsible AI in animal farming represents a paradigm shift in the way AI technologies are developed and deployed. It emphasizes ethics, transparency, sustainability, and collaboration among diverse stakeholders. By aligning innovation with ethical stewardship, responsible AI ensures that AI-driven decisions in animal farming prioritize the well-being of animals, the interests of human stakeholders, and the sustainability of agricultural practices. In this holistic pursuit, AI emerges not only as a tool for efficiency but as a force for positive change in the intricate landscape of modern animal farming.

7 Social Implications of Technology Adoption in Farming Communities

The adoption of sensor technology and AI can also have significant social implications within farming communities. These include changes in farm labor, the dynamics of farming communities, and potential disparities in technology access.

7.1 Changes in Farm Labor

The integration of advanced technologies in farming could lead to shifts in the labor landscape. Routine monitoring tasks may be automated, potentially affecting employment opportunities on farms. Conversely, technology adoption might create new roles for technology management and data analysis. Furthermore, the transition to digital farming could necessitate new skills and training for farmworkers.

7.2 Dynamics of Farming Communities

The adoption of sensor technology and AI could influence social dynamics within farming communities. Farms using advanced technologies could gain a competitive edge, potentially exacerbating disparities within the community. Furthermore, data sharing between farms could foster collaboration but might also raise concerns about competition and privacy.

7.3 Technology Access

There could be disparities in access to technology, with larger or wealthier farms more able to invest in advanced technologies. This could lead to a digital divide within farming communities, with smaller or resource-poor farms being left behind.

8 Strategies for Addressing Ethical and Social Issues

Addressing these ethical and social issues requires a multifaceted approach, integrating ethical considerations into technology design, promoting responsible use, and fostering equitable access.

8.1 Ethical Technology Design

Ethics should be a key consideration in the design of sensor technologies and AI algorithms. This could involve designing wearables that minimize discomfort or stress for animals, implementing robust data security measures, and developing transparent and fair AI algorithms.

8.2 Guidelines for Responsible Use

Clear guidelines and regulations should be established for the responsible use of sensor technologies and AI in farming. This could involve rules on data ownership, usage, and sharing, and guidelines on the humane use of surveillance technologies. Farmers should be provided with the knowledge and tools to use these technologies responsibly and effectively.

8.3 Equitable Access

Efforts should be made to ensure that all farms can benefit from the potential of sensor technologies and AI. This could involve subsidies or financing options for technology adoption, tailored solutions for small or resource-poor farms, and support services to assist all farmers in integrating these technologies into their operations.

The ethical and social considerations of using sensor technologies and AI in livestock farming are as crucial as the technical aspects. By addressing these considerations, we can ensure that the digital transformation of farming aligns with our ethical obligations to animals, respects the rights and needs of farmers, and fosters social equity within farming communities. This will require collaboration between farmers, technologists, ethicists, and policymakers, and a shared commitment to a future of farming that is not only efficient and productive, but also humane, fair, and socially sustainable. As we navigate the exciting frontier of digital farming, we must ensure that our journey is guided by a compass of ethical and social responsibility.

9 Human-Centered AI in Modern Animal Farming: Building an Ecosystem of Trust

In the dynamic realm of artificial intelligence, the pursuit of ‘Human-centered AI for an inclusive society’ emerges as a guiding principle, directing the evolution of innovations that synergize AI’s capabilities with human values and societal imperatives. This perspective underscores the crafting of AI technologies that not only respect and uphold public values, constitutional rights, and human rights but also actively reinforce them. The overarching ambition is to cultivate an ecosystem of trust, ensuring that AI applications garner public endorsement and integrate seamlessly into societal structures.

Central to this approach is the formulation of AI solutions that are both generalizable and scalable. This ensures that innovations transcend niche applications and find relevance across diverse sectors, amplifying their beneficial impact. A paramount challenge in this journey is preserving significant human control over AI-powered systems. As we bestow increased autonomy upon machines, it becomes crucial to ensure that human dignity remains uncompromised, and that the essence of human intervention remains intact.

Within the specific milieu of modern animal farming, the aspiration is to establish a resilient, consultative ecosystem that promotes the judicious use of AI. This ecosystem would not only pioneer and evaluate human-centered, value-driven AI applications but also lay down robust ethical and legal frameworks to guide their utilization. The focus is on harmonizing technological advancements with ethical considerations, positioning AI as an instrument of enhancement rather than discord.

A vital element of this vision is the integration of Values, Integrity, Social, Technological, and Accountability (VISTA) facets into the AI developmental trajectory. By delving deep into the challenges and opportunities VISTA presents in relation to AI applications for sustainable food systems, we can sculpt methodologies that are both groundbreaking and ethically aligned. The initiative to devise a VISTA lab methodology stands as a testament to this commitment. This methodology would comprise a comprehensive toolkit, ethical and legal guidelines, and engagement strategies tailored for the agri-food sector. Through a series of case studies, the objective is to embed VISTA considerations into the DNA of AI design and iterative processes.

The odyssey towards responsible and trustworthy AI in sustainable food systems is both multifaceted and promising. By championing a human-centered ethos and intertwining ethical, legal, and social reflections, we chart a course for innovations that echo societal values, engender trust, and usher the agri-food sector into a future characterized by responsibility and advancement.

10 Conclusions

The integration of sensor technologies and artificial intelligence into livestock farming holds tremendous promise for advancing animal welfare. By providing real-time, objective, and comprehensive assessments of animal health and wellbeing, these tools can catalyze a shift towards more proactive, personalized, and humane farming practices. However, the realization of this potential relies heavily on adopting a farmer-centric approach, ensuring that these technologies genuinely serve the needs of farmers and respect their unique knowledge and experience.

At the same time, the increasing reliance on digital technologies brings new ethical and social considerations to the fore. Animal privacy, data security, and the responsible use of AI are fundamental issues that must be carefully navigated. As we dive deeper into the era of digital livestock farming, our ethical commitment to both animals and farmers must remain unwavering.

Moreover, the advent of these technologies could have profound implications for farming communities, from changing labor needs to shifting social dynamics. It is crucial to ensure that the transition to digital farming is not only technologically sound and ethically responsible, but also socially equitable.

Addressing these multifaceted challenges necessitates a collaborative and interdisciplinary approach. Farmers, technologists, researchers, policymakers, and society at large must all play a part in shaping the future of farming. Together, we can harness the potential of sensor technologies and AI in a manner that respects the dignity of animals, honors the role of farmers, and fosters a sustainable and equitable livestock industry.

Ultimately, the successful adoption of sensor technologies and AI in livestock farming is not just about the technologies themselves, but about the values, principles, and practices that guide their use. It is about creating a future of farming where technology serves as an empowering tool, not an overriding force. A future where every decision is guided by a commitment to animal welfare, farmer well-being, and social sustainability.

The exploration of sensor technologies and AI in livestock farming marks a critical juncture in the evolution of agriculture. As we stand on the brink of this exciting frontier, we must ensure that our journey is guided by a holistic vision, integrating scientific innovation, ethical responsibility, and social equity. This is the pathway to a future of livestock farming where technology, animals, and humans harmoniously coexist, fostering a symbiotic relationship that nurtures the welfare of all.