Abstract
In almost every sector, data-driven business, the digitization of the data has generated a data tsunami. In addition, man-to-machine digital data handling has magnified the information wave by a large magnitude. There has been a pronounced increase in digital applications in agricultural management, which has impinged on information and communication technology (ICT) to provide benefits for both producers and consumers as well as leading to technological solutions being pushed into a rural setting. This paper showcases the potential ICT technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimization. Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture.
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References
United Nations (2019) Department of Economic Affairs Social, Division Population. World population prospects 2019: highlights
Nation United (2017). Sustainable development goals. https://sdgs.un.org/goals. Accessed 18 Nov 2021
Food and Agriculture Organization of the United Nations—FAO (2019) Strengthened global partnerships needed to end hunger and malnutrition. http://www.fao.org/news/story/en/item/1194310/icode/. Accessed 14 Aug 2021.
Trendov NM, Varas S, Zeng M (2019) Digital technologies in agriculture and rural areas—status report, Tech. Rep., Nations. Rome, Italy. Food and Agriculture Organization of the United, pp 1–19
OECD (2020) Food, A. O. Of the United Nations, OECD-FAO agricultural outlook 2020–2029. https://doi.org/10.1787/1112c23b-en. https://www.oecd-ilibrary.org/content/publication/1112c23b-en. Accessed 10 Aug 2021
Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: a comprehensive review. Artif Intell Agric 4:81–95
Varga P, Plosz S, Soos G, Hegedus. Security threats and issues in automation Io. In: IEEE international workshop on factory communication systems proceedings, WFCS, IEEE. Trondheim, Norway, ISBN 9781509057887, pp 6–19
Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B (2019) A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 1:2169–2193
Curiac D (2016) Towards wireless sensor, actuator and robot networks: conceptual framework, challenges and perspectives. J Netw Comput Appl 63:14–23
Vij A, Vijendra S, Jain A, Bajaj S, Bassi A, Sharma A (2020) IoT and machine learning approaches for automation of farm irrigation system. Procedia Comput Sci 167:1250–1257
Matei O, Rusu T, Petrovan A, Mihut G (2017) A data mining system for real-time soil moisture prediction. Procedia Eng 181:837–844
Walter A, Finger R, Huber R, Buchmann N (2017) Opinion: smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci USA 24:114–124
Chiaraviglio L, Blefari-Melazzi N, Liu W, Gutierrez JA, Beek JD, Birke R, Chen L, Idzikowski F, Kilper D, Monti P (2017) Bringing 5G into rural and low-income areas: is it feasible? IEEE Commun Stand Mag 1(3):50–57
Eurostat, Study on Broadband Coverage in Europe (2017), Tech. 665 rep., EU commission. https://ec.europa.eu/digital-single-market/en/news/study-broadband-coverage-europe-2017. Accessed 18 Nov 2021
Bacco M, Berton A, Ferro E, Gennaro C, Gotta A, Matteoli S, Paonessa F, Ruggeri M, Virone G, Zanella A (2018) Smart farming: opportunities, challenges and technology enablers. In: IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany). IEEE, pp 685: 1–6
Mekala MS, Viswanathan P (2017) A survey: smart agriculture IoT with cloud computing. In: International conference on microelectronic devices, circuits and systems. ICMDCS Vellore, India. IEEE, pp 1–7
Reddy KSP, Roopa YM, Rajeev KLN, Nandan NS (2020) IoT based smart agriculture using machine learning. In: Second international conference on inventive research in computing applications (ICIRCA), pp 130–134
High-Level Expert Forum—Global Agriculture Towards 2050 (2009) Retrieved from http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf. Accessed 08 Nov 2021
Foley J (2019) A five-step plan to feed the world. https://www.nationalgeographic.com/foodfeatures/feeding-9-billion/. Accessed 10 Nov 2021
Goldewijk K, Beusen KA, Doelman J, Stehfest E (2010) New anthropogenic land-use estimates for the Holocene. History Database of the Global Environment (HYDE), Bilthoven
AO (2016) AQUASTAT database. http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en. Accessed 18 July 2021s
Glaroudis D, Iossifides A, Chatzimisios P (2020) Survey, comparison and research challenges of IoT application protocols for smart farming. Comput Netw 107037(168):183
Ahmed H, Juraimi AS, Hamdani SM (2016) Introduction to robotics agriculture in pest control: a review. Pertanika J Sch Res Rev 2(2):80–93
Shylaja SL, Fairooz S, Venkatesh J, Sunitha D, Rao RP, Prabhu MR (2019) IoT-based crop monitoring scheme using smart device with machine learning methodology. J Phys 012019:1–12
Cox S (2002) Information technology: the global key to precision agriculture and sustainability. Comput Electron Agric 36(2):93–111
Ullah A, Ahmad J, Muhammad K, Lee MY (2017) A survey on precision agriculture: technologies and challenges. In: The 3rd international conference on next generation computing (ICNGC2017b), pp 1–3
Mahajan S, Das A, Sardana HK (2015) Image acquisition techniques for assessment of legume quality. Trends Food Sci Technol 42(2):116–133
Suchithra MS, Pai ML (2019) Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf Process Agric 7(1):72–82
Yang M, Xu D, Chen S, Li H, Shi Z (2019) Evaluation of machine learning approaches to predict soil organic matter and pH using vis-NIR spectra. Sensors 19(2):263–277
Morellos A, Pantazi X, Moshou D, Alexandridis T, Whetton R, Tziotzios G, Wiebensohn J, Bill R, Mouazen AM (2016) Machine learning-based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosyst Eng 21(52):104–116
Huang S, Fan X, Sun L, Shen Y, Suo X (2019) Research on classification method of maize seed defect based on machine vision. J Sens 2716975:1–31
Zhu S, Zhou L, Gao P, Bao Y, He Y, Feng L (2019) Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties. Molecules 24:3268–3291
Veeramani B, Raymond JW, Chanda P (2018) DeepSort: deep convolutional networks for sorting haploid maize seeds. BMC Bioinform 19(9):289–319
Keling TU, Linjuan LI, Liming Y, Jianhua W, Qun S (2018) Selection for high-quality pepper seeds by machine vision and classifiers. J Integr Agric 17(9):1999–2006
Gulve PP, Tambe SS, Pandey MA, Kanse SS (2015) Leaf disease detection of the cotton plant using image processing techniques. IOSR J Electron Commun Eng 41:50–54
Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: International conference on computing communication control and automation. IEEE Computer Society, pp 768–771
Guo Y, Zhang J, Yin C, Hu X, Zou Y, Xue Z, Wang W (2020) Plant disease identification based on deep learning algorithm in smart farming. Discret Dyn Nat Soc 2479172:1–11
Phadikar S, Sil J (2008) Rice disease identification using pattern recognition techniques. In: 11th international conference on computer and information technology. https://doi.org/10.1109/iccitechn.2008.4803079.
Mehra T, Kumar V, Gupta P (2016) Maturity and disease detection in tomato using computer vision. In: Fourth international conference on parallel, distributed and grid computing (PDGC). https://doi.org/10.1109/pdgc.2016.7913228.
Schor N, Bechar A, Ignat T, Dombrovsky A, Elad Y, Berman S (2016) Robotic disease detection in greenhouses: combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robot Autom Lett 1(1):354–360
Bhange M, Hingoliwala HA (2014) Smart farming: pomegranate disease detection using image processing. Procedia Comput Sci 58:280–288
Omrani E, Khoshnevisan B, Shamshirband S, Saboohi H, Anuar NB, Nasir MHNM (2014) Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55:512–519
Bashir S, Sharma N (2012) Remote area plant disease detection using image processing. IOSR J Electron Commun Eng 2(6):31–34
Castro D, New J (2016) The promise of artificial intelligence. Center for Data Innovation, pp 1–48
Golhani K, Balasundram SK, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. Inf Process Agric 5:354–371
Moshou D, Bravo C, West J, Wahlen S, McCartney A, Ramon H (2004) Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Comput Electron Agric 44:173–188
Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using a pre-trained deep learning algorithm. Procedia Comput Sci 133:1040–1047
Backhaus A, Bollenbeck F, Seiffert U (2011) Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. In: 3rd workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS)
Chung CL, Huang KJ, Chen SY, Lai MH, Chen YC, Kuo YF (2016) Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric 121:404–411
Ataş M, Yardimci Y, Temizel A (2012) A new approach to aflatoxin detection in chili pepper by machine vision. Comput Electron Agric 87:129–141
Habib MT, Majumder A, Jakaria AZM, Akter M, Uddin MS, Ahmed F (2018) Machine vision-based papaya disease recognition. J King Saud Univ-Comput Inf Sci 32(3):300–309
Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf Process Agric 7(3):418–426
Gavhale RK, Gawande U, Hajari KO (2014) Unhealthy region of citrus leaf detection using image processing techniques. In: International conference for convergence for technology. IEEE, pp 76–86
Sreedhar B, Kumar MS (2020) A comparative study of melanoma skin cancer detection in traditional and current image processing techniques. In: Fourth international conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud)(I-SMAC). IEEE, pp 654–658
Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc 12(6):1038–1048
Malchi SK, Kallam S, Al-Turjman F, Patan R (2021) A trust-based fuzzy neural network for smart data fusion in the internet of things. Comput Electr Eng 89(106901):1–35
Selim H (2018) Recognition and detection of tea leaf’s diseases using support vector machine. In: IEEE 14th international colloquium on signal processing & its applications (CSPA)
Natarajan VA, Kumar MS, Patan R, Kallam S, Mohamed MYN (2020) Segmentation of nuclei in histopathology images using fully convolutional deep neural architecture. In: International conference on computing and information technology (ICCIT-1441). IEEE, pp 1–7
Agrawal N, Singhai J, Agarwal DK (2017) Grape leaf disease detection and classification using multi-class support vector machine. In: International conference on recent innovations in signal processing and embedded systems (RISE), pp 14–23
Sangamithra B, Neelima P, Kumar MS (2017) A memetic algorithm for multi-objective vehicle routing problem with time windows. In: IEEE international conference on electrical, instrumentation and communication engineering (ICEICE), pp 1–8
Neelakantan P (2021) Analyzing the best machine learning algorithm for plant disease classification. Mater Today 1–4
Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80(103615):1–11
Reddy TV, Sashirekhak K (2020) Examination on advanced machine learning techniques for plant leaf disease detection from leaf imagery. J Crit Rev 7(5):1208–1221
Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272–279
Hossain E, Hossain MF, Rahaman MA (2019) A color and texture-based approach for the detection and classification of plant leaf disease using KNN classifier. In: International conference on electrical, computer and communication engineering (ECCE). IEEE, pp 1–6
Gupta D, Sharma P, Choudhary K, Gupta K, Chawla R, Khanna A, Albuquerque VHCD (2020) Artificial plant optimization algorithm to detect infected leaves using machine learning. Expert Syst 38:e12501
Arora J, Agrawal U (2020) Classification of Maize leaf diseases from healthy leaves using Deep Forest. J Artif Intell Syst 2(1):14–26
Zhang S, Zhang S, Zhang C, Wang X, Shi Y (2019) Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput Electron Agric 162:422–430
Cruz AC, Luvisi A, De Bellis L, Ampatzidis Y (2017) X-FIDO: an effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front Plant Sci 8:1–12
Cruza A, Ampatzidisb Y, Pierro R, Materazzi A, Panattoni A, Bellis LD, Luvisi A (2019) Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Comput Electron Agric 157:63–76
Kaur S, Pandey S, Goel S (2018) A semi-automatic leaf disease detection and classification system for soybean culture. IET Image Process 12(6):45–67
Sengar N, Dutta MK, Travieso CM (2018) Computer vision-based technique for identification and quantification of powdery mildew disease in cherry leaves. Computing 1–13
Janarthan S, Thuseethan S, Rajasegarar S, Lyu Q, Zheng Y, Yearwood J (2020) Deep metric learning based citrus disease classification with sparse data. IEEE Access 8:162588–162600
Ali H, Lali MI, Nawaz MZ, Sharif M, Saleem BA (2017) Symptom-based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104
Bah MD, Hafiane A (2018) Canals R: Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sens 10:1690
Zhang X, Han L, Dong Y, Shi Y, Huang W, Han L, Moreno GP, Ma H, Ye H, Sobeih TA (2019) Deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens 11:1554
Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, Blomme G (2019) AI-powered banana diseases and pest detection. Plant Methods 15:92
Singh S, Singh NP (2019) Machine learning-based classification of good and rotten apple. In: Recent trends in communication, computing, and electronics. Lecture notes in Electrical Engineering. Springer, Singapore, p 524
Saraansh B, Siddhant K, Anuja A (2019) Deep learning convolutional neural network for apple leaves disease detection. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM). Amity University Rajasthan, Jaipur
Gargade A, Khandekar S (2021) Custard apple leaf parameter analysis, leaf diseases, and nutritional deficiencies detection using machine learning. In: Advances in signal and data processing. Lecture Notes in Electrical Engineering. Springer, Singapore, p 703
Sharifa M, Khana MA, Iqbal Z, Azam MF, Lalib MIU, Younus M (2018) “Javed Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234
Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: Conference on advances in signal processing (CASP), pp 175–179
Warne PP, Ganorkar SR (2015) Detection of diseases on cotton leaves using K-mean clustering method. Int Res J Eng Technol 2(4):1–29
Kaur R, Singla S (2016) Classification of plant leaf diseases using gradient and texture feature. In: International conference on advances in information communication technology & computing, pp 96–107
Patil SP, Zambre SR (2014) Classification of cotton leaf spot disease using support vector machine. Int J Eng Res Appl 4:92–97
Revathi P, Hemalatha M (2012) Classification of cotton leaf spot diseases using image processing edge detection techniques. In: InInternational conference on emerging trends in science, engineering and technology (INCOSET), pp 169–173
Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural networks. In: Fourth international conference on computing, communications and networking technologies (ICCCNT), pp 1–5
Dubey SR, Jalal AS (2012) Detection and classification of apple fruit diseases using complete local binary patterns. In: Third international conference on computer and communication technology, pp 346–351
Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96–107
Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10:11–32
Kour V, Arora S (2018) Fruit disease detection using rule-based classification. In: Proceedings of smart innovations in communication and computational sciences, advances in intelligent systems and computing (ICSICCS-2018), pp 295–312
Turkoglu M, Hanbay D (2019) Plant disease and pest detection using deep learning-based features. Turk J Electr Eng Comput Sci 27:1636–1651
Moshou D, Bravo C, Oberti R, West J, Bodria L, McCartney A, Ramon H (2005) Plant disease detection based on data fusion of hyperspectral and multispectral fluorescence imaging using Kohonen maps. Real-Time Imaging 11(2):75–83
Andrei BB, Torres B, Atslands R, Rocha R, Ticiana L, Silva CD, Souza JN, Gondim RS (2020) Multilevel data fusion for the internet of things in smart agriculture. Comput Electron Agric 171(105309):1–16
Zhu N, Liu X, Liu Z, Hu K, Wang Y, Tan J, Huang M, Zhu Q, Ji X, Jiang Y, Guo Y (2018) Deep learning for smart agriculture: concepts, tools, applications, and opportunities. Int J Agric Biol Eng 11(4):32–44
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks-based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 3289801:11–41
Tang JL, Wang D, Zhang ZG, He L, Xin J, Xu Y (2017) Weed identification based on K-means feature learning combined with the convolutional neural network. Comput Electron Agric 135:63–70
Yalcin H (2017) Plant phenology recognition using deep learning. In: 6th international conference on deep-pheno. Agro-Geoinformatics. IEEE, pp 31–44
Namin ST, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO (2017) Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods 134(205):1–37
Minh DHT, Ienco D, Gaetano R, Lalande N, Ndikumana E (2017) Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR Sentinel. IEEE Geosci Remote Sens Lett 99:1–5
Bu F, Wang X (2019) A smart agriculture IoT system based on deep reinforcement learning. Futur Gener Comput Syst 99:500–507
Yu N, Li L, Schmitz N, Tian LF, Greenberg JA, Diers BW (2016) Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle-based platform. Remote Sens Environ 187:91–101
Chu Z, Yu J (2020) An end-to-end model for rice yield prediction using deep learning fusion. Comput Electron Agric 174:105471
Oliveira DT, Silva RP, Maldonado JW, Zerbato C (2020) Convolutional neural networks in predicting cotton yield from images of commercial fields. Comput Electron Agric 171:105307
Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104859
Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi FB (2020) Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens Environ 237:111599
Yang Q, Shi L, Han J, Zha Y, Zhu P (2019) Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Res 235:142–153
Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621–637
Rahnemoonfar M, Sheppard C (2017) Real-time yield estimation based on deep learning. In: Autonomous air and ground sensing systems for agricultural optimization and phenotyping, vol 10218, pp 1021809–1021821
Chen Y, Lee WS, Gan H, Peres N, Fraisse C, Zhang Y, He Y (2019) Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sens 11(13):1584–1617
Sun J, Di L, Sun Z, Shen Y, Lai Z (2019) County-level soybean yield prediction using deep CNN-LSTM model. Sensors 19(20):4363–4391
Khaki S, Wang L, Archontoulis SV (2020) A cnn-rnn framework for crop yield prediction. Front Plant Sci 10:1750–1782
Terliksiz AS, Altylar DT (2019) Use Of deep neural networks for crop yield prediction: a case study Of Soybean Yield in Lauderdale County, Alabama, USA. In: 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, pp 1–4
Lee S, Jeong Y, Son S, Lee B (2019) A self-predictable crop yield platform (SCYP) based on crop diseases using deep learning. Sustainability 11(13):3637–3659
Elavarasan D, Vincent PD (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access 8:86886–88690
Wang X, Huang J, Feng Q, Yin D (2020) Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of china with deep learning approaches. Remote Sens 12(11):1744–1777
Ju S, Lim H, Heo J (2020) Machine learning approaches for crop yield prediction with MODIS and weather data. In: 40th Asian conference on remote sensing: progress of remote sensing technology for smart future, ACRS
Shin J, Chang KY, Heung B, Quang TN, Price GW, Mallahi AA (2021) A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Comput Electron Agric 183(106042):1–8
Jiang B, He J, Yang S, Fu H, Li T, Song H, He D (2019) Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artif Intell Agric 1:1–8
Durmus H, Günes EO, Kırcı M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 6th international conference on agro-geoinformatics. IEEE, pp 1–5
Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852–1875
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Fuentes A, Yoon S, Kim S, Park D (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022–2051
Mohanty PS, Hughes DP, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1–10
Guo D, Juan J, Chang L, Zhang J, Huang D (2017) Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques. Sci Rep 7(1):1–23
Jay S, Rabatel G, Hadoux X, Moura D, Gorretta N (2015) In-field crop row phenotyping from 3D modeling performed using structure from motion. Comput Electron Agric 110:70–77
Dee H, French A (2015) From image processing to computer vision: plant imaging grows up. Funct Plant Biol 42(5):1–19
Coppens F, Wuyts N, Inze D, Dhondt S (2017) Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Curr Opin Syst Biol 4:58–63
Bai G, Ge Y, Hussain W, Baenziger PS, Graef G (2016) A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Comput Electron Agric 128:181–192
Mahlein AK, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, Kersting K (2019) Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! Curr Opin Plant Biol 50:156–162
Thomas S, Behmann J, Steier A, Kraska T, Muller O, Rascher U, Mahlein AK (2018) Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 14(1):1–31
Singh V, Misra AKV (2015) Detection of the unhealthy region of plant leaves using image processing and genetic algorithm. In: International conference on advances in computer engineering and applications, pp 197–209
Mueller-Sim T, Jenkins M, Abel J, Kantor G (2017) The Robotanist: a ground-based agricultural robot for high-throughput crop phenotyping. In: IEEE international conference on robotics and automation (ICRA)https://doi.org/10.1109/icra.2017.7989418
Naito H, Ogawa S, Valencia MO, Mohri H, Urano Y, Hosoi F, Omasa K (2017) Estimating rice yield-related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. ISPRS J Photogramm Remote Sens 125:50–62
Shafiekhani A, Kadam S, Fritschi F, DeSouza G (2017) Vinobot and vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors 17(12):214–241
Deery D, Jimenez-Berni J, Jones H, Sirault X, Rurbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4(3):349–379
Ubbens JR, Stavness I (2017) Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci 8:90–111
Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linowi M, Zhoua J, Tardieu F (2019) What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Sci 282:14–22
Feng L, Chen S, Zhang C, Zhang Y, He Y (2021) A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Comput Electron Agric 182:1–32
Rousseau D, Dee H, Pridmore T (2015) Imaging methods for phenotyping of plant traits. Phenomics in crop plants: trends, options, and limitations. Springer, Berlin, pp 61–74
Shakoor N, Lee S, Mockler TC (2017) High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol 38:184–192
Zhou C, Ye H, Hu J, Shi X, Hua S, Yue J, Xu Z, Yang G (2019) Automated counting of rice panicle by applying deep learning model to images from unmanned aerial vehicle platform. Sensors 19(14):3106–3131
Liu Y, Noguchi N, Liang L (2019) Development of a positioning system using UAV-based computer vision for airboat navigation in paddy field. Comput Electron Agric 162:126–133
Barrero O, Perdomo SA (2018) RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precis Agric 19(5):809–822
Li Y, Qian M, Liu P, Cai Q, Li X, Guo J, Yan H, Yu F, Yuan K, Yu J (2019) The recognition of rice images by UAV based on capsule network. Clust Comput 22(4):9515–9524
Kitpo N, Inoue M (2018) Early rice disease detection and position mapping system using drone and IoT architecture. In: 12th South East Asian Technical University Consortium (SEATUC). IEEE, vol 1, pp 1–5
Qin WC, Qiu BJ, Xue XY, Chen C, Xu ZF, Zhou QQ (2016) Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against planthoppers. Crop Prot 85:79–88
Boniecki P, Koszela K, Piekarska-Boniecka H, Weres J, Zaborowicz M, Kujawa S, Majewski A, Raba B (2015) Neural identification of selected apple pests. Comput Electron Agric 110:9–16
Tripathy AK, Adinarayana J, Merchant DS, Desai SN, Vijayalakshmi UB, Reddy K, Sreenivas DR, Ninomiya G, Hirafuji SM (2011) Data mining and wireless sensor network for agriculture pest/disease predictions. In: 2011 World Congress on Information and Communication Technologies (WICT), pp 1229–1234
Rodrigues LM, Dimuro GP, Franco DT, Fachinello JC (2013) A system based on interval fuzzy approach to predict the appearance of pests in agriculture. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp 1262–1267
Rupnik R, Kukar M, Vracar P, Kosir D, Pevec D, Bosnic Z (2018) AgroDSS: a decision support system for agriculture and farming. Comput Electron Agric 161:260–271
Lottes P, Hoeferlin M, Sander S, Müter M, Schulze P, Stachniss LC (2016) An effective classification system for separating sugar beets and weeds for precision farming applications. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 5157–5163
Yu J, Sharpe SM, Schumann AW, Boyd NS (2019) Deep learning for image-based weed detection in turfgrass. Eur J Agron 104:78–84
Bosilj P, Duckett T, Cielniak G (2018) Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture. Comput Ind 98:226–240
Padalalu P, Mahajan S, Dabir K, Mitkar S, Javale D (2017) Smart water dripping system for agriculture/farming. In: 2nd International Conference for Convergence in Technology (I2CT), pp 659–662
Albanese A, Nardello M, Brunelli D (2021) Automated pest detection with DNN on the edge for precision agriculture. IEEE J Emerg Sel Topics Circ Syst 11(3):458–467
Segalla A, Fiacco G, Tramarin L, Nardello M, Brunelli D (2020) Neural networks for pest detection in precision agriculture. In: Proc. IEEE Int. Workshop Metrol. Agricult. Forestry (MetroAgriFor), pp 7–12
Lima MCF, Leandro MEDA, Valero C, Coronel LCP, Bazzo COG (2020) Automatic detection and monitoring of insect pests—a review. Agriculture 10(5):161–179
Panchal AV, Patel SC, Bagyalakshmi K, Kumar P, Khan IR, Son M (2021) Image-based plant diseases detection using deep learning. Mater Today 1–7
Barbedo JG, Arnal A (2014) An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis 98(12):1709–1716
Hiary HA, Ahmad SB, Reyalat M, Braik M, Rahamneh ZAL (2011) Fast and accurate detection and classification of plant diseases. Int J Comput Appl 17(1):31–38
Mokhtar U, Ali MA, Hassanien AE, Hefny H (2015) Identifying two of tomatoes leaf viruses using support vector machine. In: Information systems design and intelligent applications. Springer, pp 771–782
Arivazhagan S (2013) Detection of the unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217
Jiang H, Xiaoru L, Safara F (2021) IoT based Agriculture: deep learning in detecting apple fruit diseases. Microprocess Microsyst 1–23
Peng J (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080
Karar ME, Alsunaydi F, Albusaymi S, Alotaibi S (2021) A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alex Eng J 60:4423–4432
Butera L, Ferrante A, Jermini M, Prevostini M, Alippi C (2021) Precise agriculture: effective deep learning strategies to detect pest insects. IEEE/CAA J Autom Sin 9(2):246–258
Sanga SL, Machuve D, Jomanga K (2020) Mobile-based deep learning models for Banana disease detection. Technol Appl Sci Res 10(3):5674–5677
Chohan M, Khan A, Katper S, Mahar M (2020) Plant disease detection using deep learning. Int J Recent Technol Eng 9(1):909–914
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1–10
Mohameth F, Bingcai C, Sada KA (2020) Plant disease detection with deep learning and feature extraction using Plant Village. J Comput Commun 8(6):10–22
Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S, Bhardwaj S (2020) Potato leaf diseases detection using deep learning. In: 4th international conference on intelligent computing and control systems (ICICCS). IEEE, Madurai, India, pp 461–466
Khamparia A, Saini G, Gupta D, Khanna A, Tiwari S, Albuquerque VHC (2020) Seasonal crops disease prediction and classification using deep convolutional encoder network. Circ Syst Sign Process 39:818–836
Bedi P, Gole P (2021) Plant disease detection using hybrid model based on convolutional autoencoder and convolution neural network. Artif Intell Agric 5:90–101
Perez-Ortiz M, Gutiérrez PA, Pena JM, Sánchez JT, Granados FL, Martínez CH (2016) Machine learning paradigms for weed mapping via unmanned aerial vehicles. In: IEEE symposium series on computational intelligence (SSCI), Athens, pp 1–8
Suit ST, Kumaraswamy R (2019) Performance comparison of weed detection algorithms. In: International conference on communication and signal processing (ICCSP). Chennai, India
Alam M, Alam MS, Roman M, Tufail M, Khan MU, Khan MT (2020) Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. In: Proceedings of the 7th international conference on electrical and electronics engineering (ICEEE). Antalya, Turke, pp 273–280
Tu YH, Johansen K, Phinn S, Robson A (2019) Measuring canopy structure and condition using multi-spectral UAS imagery in a horticultural environment. Remote Sens 11:269–299
Gao J, Nuyttens D, Lootens P, He Y, Pieters JG (2018) Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng 170:39–50
Castro D, Torres-Sanchez AI, Peña J, Jiménez-Brenes JM, Csillik FM, Granados OL (2018) An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens 10:285–3015
Etienne A, Saraswat D (2019) Machine learning approaches to automate weed detection by UAV based sensors. In: Autonomous air and ground sensing systems for agricultural optimization and phenotyping IV. International Society for Optics and Photonics, Bellingham, vol 11008, pp 110080–110087
Chabot D, Dillon C, Shemrock A, Weissflog N, Sager EP (2018) An object-based image analysis workflow for monitoring shallow-water aquatic vegetation in multispectral drone imagery. ISPRS Int J Geo-Inf 7:294–318
Brinkhoff J, Vardanega J, Robson AJ (2020) Land cover classification of nine perennial crops using sentinel-1 and-2 data. Remote Sens 12:96–129
Zhang S, Guo J, Wang Z (2019) Combing K-means clustering and local weighted maximum discriminant projections for weed species recognition. Front Comput Sci 1(4):1–29
Bakhshipour A, Jafari A (2018) Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Comput Electron Agric 145:153–160
Abouzahir S, Sadik M, Sabir E (2018) Enhanced approach for weeds species detection using machine vision. In: Proceedings of the 2018 international conference on electronics, control, optimization and computer science (ICECOCS). Kenitra, Morocco, pp 1–6
Ortiz MP, Pena JM, Gutierrez PA, Sanchez JT, Martínez CH, Granados LF (2016) Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Syst 47:85–94
Ahmed F, Al-Mamun HA, Bari AH, Hossain E, Kwan P (2012) Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot 40:98–104
Khan Y, See CS (2016) Predicting and analyzing water quality using Machine Learning: a comprehensive model. In: IEEE long island systems, applications and technology conference (LISAT), Farmingdale, pp 1–6
Machado MR, Júnior TR, Silva MR, Martins JB (2019) Smart water management system using the microcontroller ZR16S08 as IoT solution. In: IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS). Armenia, Colombia, pp 169–172
Kamienski C, Soininen J, Taumberger M, Dantas R, Toscano A, Cinotti TS, Maia RF, Neto AT (2019) Smart water management platform: IoT-based precision irrigation for agriculture. Sensors 19(2):1–20
Perea RG, Poyato EC, Montesinos P, Díaz JAR (2019) Prediction of irrigation event occurrence at farm level using optimal decision trees. Comput Electron Agric 157:173–180
Xie T, Huang Z, Chi Z, Zhu T (2017) Minimizing amortized cost of the on-demand irrigation system in smart farms. In: 3rd international workshop on cyber-physical systems for smart water networks, pp 43–46
Kokkonis G, Kontogiannis S, Tomtsis D (2017) FITRA: a neuro-fuzzy computational algorithm approach based on an embedded water planting system. In: 2nd international conference on internet of things, data and cloud computing, vol 39, pp 1–39
Chen H, Chen A, Xu L, Xie H, Qiao H, Lin Q, Cai K (2020) A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manag 240(106303):1–8
Goldstein A, Fink L, Meitin A, Bohadana S, Lutenberg O, Ravid G (2018) Applying machine learning on sensor data for irrigation recommendations: revealing the agronomists tacit knowledge. Precis Agric 19:421–444
Abdullah N, Durani N, Shari MFB, Siong KS, Hau VKW, Siong WN, Ahmad IRKA (2021) Towards smart agriculture monitoring using fuzzy systems. IEEE Access 9:4097–4111
García L, Parra L, Jimenez JM, Lloret J, Lorenz P (2020) IoT-Based smart irrigation systems: an overview on the recent trends on sensors and iot systems for irrigation in precision agriculture. Sensors 20:1042–1068
Abioye EA, Abidin MSZ, Mahmud MSA, Buyamin S, Ishak MHI, Rahman MKIA (2020) A review on monitoring and advanced control strategies for precision irrigation. Comput Electron Agric 173(105441):1–37
Ray PP (2017) Internet of things for smart agriculture: technologies, practices and future direction. J Ambient Intell Smart Environ 9:395–420
Lamas PF, Echarri CM, Azpilicueta L, Iturri LP, Falcone F, Carames F (2020) Design and empirical validation of a LoRaWAN IoT smart irrigation system. In: Proc AMIA Annu Fall Symp, vol 42, p 62
Han L, Srocke F, Masek O, Smith DL, Lafond JA, Allaire S (2020) A graphical-user-interface application for multifractal analysis of soil and plant structures. Comput Electron Agric 174(105454):1–29
Zaragoza AC, Perea GR, García FI, Poyato CE, Díaz RJA (2020) Open source application for optimum irrigation and fertilization using reclaimed water in olive orchards. Comput Electron Agric 2173(105407):1–27
Jiang JA, Wang CW, Liao MS, Zheng XY, Liu JH, Chuang CL (2016) A wireless sensor network-based monitoring system with dynamic converge cast tree algorithm for precision cultivation management in orchid greenhouses. Precis Agricu 17:766–785
Syifa M, Park SJ, Wook C (2020) Lee detection of the pine wilt disease tree candidates for drone remote sensing using artificial intelligence techniques. Engineering 6:919–926
Oca AM, Flores G (2021) The AgriQ: a low-cost unmanned aerial system for precision agriculture. Expert Syst Appl 182(115163):1–19
Jayaraman P, Yavari A, Georgakopoulos D, Morshed A, Zaslavsky A (2016) Internet of things platform for smart farming: experiences and lessons learned. Sensors 16:1884–1907
Pereira WF, Fonseca LS, Putti FF, Goes BC, Naves LP (2020) Environmental monitoring in a poultry farm using an instrument developed with the internet of things concept. Comput Electron Agric 170(105257):1–20
Ramli MR, Daely PT, Kim DS, Lee JM (2020) IoT-based adaptive network mechanism for reliable smart farm system. Comput Electron Agric 170(105287):1–17
Deng F, Zuo P, Wen K, Wu X (2020) Novel soil environment monitoring system based on RFID sensor and LoRa. Comput Electron Agric 169(105169):1–25
Sai Z, Fan Y, Yuliang T, Lei X, Yifong Z (2016) Optimized algorithm of sensor node deployment for intelligent agricultural monitoring. Comput Electron Agric 127:76–86
Pastor FF, Chamizo GJ, Hidalgo NM, Martínez MJ (2018) Precision agriculture design method using a distributed computing architecture on the internet of things context. Sensors 18:1731–1749
Thakur D, Kumar Y, Vijendra S (2020) Smart irrigation and intrusions detection in agricultural fields using I. Proced Comput Sci 167:154–162
Anand J, Perinbam JRP (2014) Automatic irrigation system using fuzzy logic. AEIJMR 2:1–9
Mousa AK, Croock MS, Abdullah MN (2014) Fuzzy-based decision support model for irrigation system management. Int J Comput Appl 104:14–20
Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61(1):63–78
Lee WS, Slaughter DC, Giles DK (1999) Robotic weed control system for tomatoes. Precis Agric 1:95–113
Agrobot (2019) http://agrobot.com/. Accessed 01 Nov 2021
Adamides G, Katsanos C, Christou G, Xenos M, Papadavid G, Hadzilacos T (2014) User interface considerations for telerobotics: the case of an agricultural robot sprayer. In: Second international conference on remote sensing and geoinformation of the environment (RSCy2014), pp 17–28
Bao Y, Tang L, Breitzman MW, Fernandez MGS, Schnable PS (2019) Field-based robotic phenotyping of sorghum plant architecture using stereo vision. J Field Robot 36(2):397–415
Hajjaj SSH, Sahari KSM (2016) Review of agriculture robotics: practicality and feasibility. In: IEEE international symposium on robotics and intelligent sensors (IRIS). IEEE, pp 90–99
Xiong Y, Ge Y, Grimstad L, From PJ (2020) An autonomous strawberry-harvesting robot: design, development, integration, and field evaluation. J Field Robot 37(2):34–57
Silwal A, Davidson JR, Karkee M, Mo C, Zhang Q, Lewis KM (2017) Design, integration, and field evaluation of a robotic apple harvester. J Field Robot 34(6):1140–1159
Pire T, Mujica M, Civera J, Kofman E (2019) The Rosario dataset: multisensor data for localization and mapping in agricultural environments. Int J Robot Res 2(7):83–107
Ramesh MV (2017) Water quality monitoring and waste management using IoT. In: IEEE global humanitarian technology conference (GHTC), San Jose, CA, pp 1–7
Aliac CJG, Maravillas E (2018) IOT hydroponics management system. In: IEEE 10th international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM). Baguio City, Philippines, pp 1–5
Rao RN, Sridhar B (2018) IoT based smart crop field monitoring and automation irrigation system. In: 2nd international conference on inventive systems and control (ICISC). Coimbatore, pp 478–483
Waheed T, Bonnell RB, Prasher SO, Paulet E (2006) Measuring performance in precision agriculture: CART—a decision tree approach. Agric Water Manag 84:173–185
Goel P, Prasher S, Landry J, Patel R, Bonnell R, Viau A, Miller J (2003) Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Comput Electron Agric 38(2):99–124
Mercier G, Lennon M (2013) Support vector machines for hyperspectral image classification with spectral-based kernels. IGARSS. In: IEEE International Geoscience and Remote Sensing Symposium. Proceedings, vol 1, pp 29–37
Rehman A, Abbasi AZ, Islam N, Shaikh ZA (2014) A review of wireless sensors and networks’ applications in agriculture. Comput Stand Interfaces 36(2):263–270
Keshtgari M, Deljoo A (2012) A wireless sensor network solution for precision agriculture based on ZigBee technology. Wirel Sens Netw 4:25–30
Bhatnagar V, Chandra R (2020) IoT-based soil health monitoring and recommendation system. Internet Things Anal Agric 2:1–21
Dasig DDJ (2020) Implementing IoT and wireless sensor networks for precision agriculture. Internet Things Anal Agric 2:23–44
Yu C (2020) Plant spike: a low-cost, low-power beacon for smart city soil health monitoring. IEEE Internet Things J 7(9):9080–9090
Nurzaman A, De D, Hussain I (2018) Internet of things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet Things J 5(6):4890–4899
Chen W (2019) AgriTalk: IoT for precision soil farming of turmeric cultivation. IEEE Internet Things J 6(3):5209–5223
Goswami V, Singh P, Dwivedi P, Chauhan S (2020) Soil health monitoring system. Int J Res Appl Sci Eng Technol 8(5):1536–1540
Sengupta A, Debnath B, Das A, De D (2021) armFox: a QuadSensor-based IoT box for precision agriculture. IEEE Consum Technol Soc 21(62):63–68
Cicioglu M, Çalhan A (2021) Smart agriculture with the internet of things in cornfields. Comput Electr Eng 90(106982):1–11
allMETEO (2019) allMETEO. https://www.allmeteo.com/. Accessed 18 Nov 2021
S Elements (2019) Smart elements. https://smartelements.io/. Accessed 11 Nov 2021
Pycno (2019) Pycno. https://www.pycno.co/. Accessed 09 Oct 2021
Farmapp (2019) Farmapp. https://farmappweb.com/. Accessed 10 Jan 2021
Growlink (2019) Growlink. http://growlink.com/. Accessed 12 March 2021
GreenIQ (2019) GreenIQ. https://easternpeak.com/works/iot/. Accessed 21 Aug 2021s
Arable (2019) Arable. https://arable.com/. Accessed 21 Aug 2021
Semios (2019) Semios. http://semios.com/. Accessed 22 July 2021
SCR/Allflex (2019) SCR/Allflex. http://www.scrdairy.com/. Accessed 30 July 2021
Cowlar (2019) Cowlar. https://cowlar.com/. Accessed 01 Dec 2021
FarmLogs (2019) FarmLogs. https://farmlogs.com/. Accessed 05 Jan 2021
Cropio (2019) Cropio. https://about.cropio.com/#agro. Accessed 19 Sept 2021
Farmshots (2019) Farmshots. http://farmshots.com. Accessed 18 Oct 2021
aWhere (2019) aWhere. https://www.awhere.com. Accessed 19 Oct 2021
Plantix (2019) Plantix. https://plantix.net/en. Accessed 07 Dec 2021
T Genomics (2019) Trace genomics. https://www.tracegenomics.com/#/. Accessed 11 Sept 2021
SkySquirrel (2019) SkySquirrel. https://www.skysquirrel.ca/#productnav. Accessed 22 Nov 2021
Spray S (2019) See & Spray. http://smartmachines.bluerivertechnology.com. Accessed 28 Nov 2021
CROO (2019) CROO. https://harvestcroo.com. Accessed 10 Jan 2021
Arable (2019) https://www.arable.com/. Accessed 10 Jan 2021
Farmers Edge (2019) https://www.farmersedge.ca/. Accessed 03 Oct 2021
Prospera (2019) https://home.prospera.ag/row-crops. Accessed 19 June 2021
Blue River Technology (2019) http://www.bluerivertechnology.com/. Accessed 19 June 2021
FarmBot (2019) https://farm.bot/. Accessed 07 April 2021
Schiller B (2017) Machine learning helps small farmers identify plant pests and diseases. Fast Company. https://www.fastcompany.com/40468146/machine-learninghelps-small-farmers-identify-plant-pests-and-diseases
FFRobotics (2019) https://www.ffrobotics.com/. Accessed 11 Dec 2021
Araby AA (2019) Smart IoT monitoring system for agriculture with predictive analysis. In: 8th international conference on modern circuits and systems technologies (MOCAST). Thessaloniki, Greece, pp 1–4
Dimitriadis S, Goumopoulos C (2008) Applying machine learning to extract new knowledge in precision agriculture applications. In: Panhellenic conference on informatics. Samos, pp 100–104
Wang P, Hafshejani BA, Wang D (2021) An improved multilayer perceptron approach for detecting sugarcane yield production in IoT-based smart agriculture. Microprocess Microsyst 82(103822):1–7
Tumer AE, Koc BA, Kocer S (2017) Artificial neural network models for predicting the energy consumption of the process of crystallization syrup in Konya sugar factory. Int J Intell Syst Appl Eng 5:18–21
Kaburlasos VG, Spais V, Petridis V, Petrou L, Kazarlis S, Maslaris N (2020) Intelligent clustering techniques for prediction of sugar production. Math Comput Simul 60:159–168
Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: a survey. Comput Electron Agric 155:257–282
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 8:1–21
Li B, Lecourt J, Bishop G (2018) Advance in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—a review. Plants 7(1):1–15
Schwalbert RA, Amado T, Corassa G, Pott LP, Prasad PV, Ciampitti IA (2020) Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric For Meteorol 284:1–29
Chu Z, Yu J (2020) An end-to-end model for rice yield prediction using deep learning fusion. Comput Electron Agric 174:1–19
Reddy DJ, Kumar MR (2021) Crop yield prediction using machine learning algorithm. In: 5th International conference on intelligent computing and control systems (ICICCS), pp 1466–1470
Elavarasan D, Vincent DRPM (2021) Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks. Neural Comput Appl 33:13205–13224
Forsythe MM (2021) Crop yield prediction using deep neural networks and LSTM. Agric Case Stud Projects Mach Learn Remote Sens 1:1–18
Jeong S, Ko J, Yeom JM (2022) Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Sci Total Environ 802:149726
Haque FF, Abdelgawad A, Yanambaka VP, Yelamarthi K (2020) Crop yield prediction using deep neural network. In: IEEE 6th world forum on Internet of Things (WF-IoT), vol 1, p 12
Nosratabadi S, Imre F, Kando K, Szell K, Regia A, Ardabili S, Mosavi A (2021) Hybrid machine learning models for crop yield prediction. https://arxiv.org/ftp/arxiv/papers/2005/2005.04155.pdf, pp 1–5
Zhou X, Lee WS, Ampatzidis Y, Chen Y, Peres N, Fraisse C (2021) Strawberry maturity classification from UAV and near-ground imaging using deep learning. Smart Agric Technol 1(100001):1–8
Sharma S, Rai S, Krishnan NC (2020) Wheat crop yield prediction using deep LSTM model CoRR abs/2011.01498. https://arxiv.org/abs/2011.01498, pp 1–8
Khan T, Qiua J, Qureshi MAA, Iqbal MS, Mehmood R, Hussain W (2019) Agricultural fruit prediction using deep neural networks. Procedia Comput Sci 174:72–78
Qiao M, He X, Cheng X, Li P, Luo H, Zhang L, Tian Z (2021) Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks. Int J Appl Earth Obs Geoinf 102(102436):1–12
Xu W, Chen P, Zhan Y, Chen S, Zhang L, Lan Y (2021) Cotton yield estimation model based on machine learning using time series UAV remote sensing data. Int J Appl Earth Obs Geoinf 104(102511):1–13
Tello JT, Ko SB (2021) Identifying useful features in multispectral images with deep learning for optimizing wheat yield prediction. In: IEEE international symposium on circuits and systems (ISCAS), pp 1–5
Wolanin A, García GM, Valls GC, Chova LG, Meroni M, Duveiller G, Guanter L (2020) Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt. Environ Res Lett 15(2):1–23
Bhojani SH, Bhatt N (2020) Wheat crop yield prediction using new activation functions in neural network. Neural Comput Appl 1–11
Fathi MT, Ezziyyani M, Ezziyyani M, Mamoune S (2019) Crop yield prediction using deep learning in Mediterranean Region. In: International conference on advanced intelligent systems for sustainable development. Springer, Cham, vol 20, pp 106–114
Shidnal S, Latte MV, Kapoor A (2019) Crop yield prediction: two-tiered machine learning model approach. Int J Inf Technol 10:1–9
Bolton DK, Friedl MA (2013) Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric For Meteorol 173:74–84
Nguyen LH, Zhu J, Lin Z, Du H, Yang Z, Guo W, Jin F (2019) Spatial-temporal multi-task learning for within-field cotton yield prediction. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Cham, pp 343–354
Alwis SD, Zhang Y, Na M, Li G (2019) Duo attention with deep learning on tomato yield prediction and factor interpretation. In: Pacific Rim international conference on artificial intelligence. Springer, Cham, pp 704–715
Jiang H, Hu H, Zhong R, Xu J, Xu J, Huang J, Lin T (2020) A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: a case study of the US Corn Belt at the county level. Glob Change Biol 26(3):1754–1766
Saravi B, Nejadhashemi AP, Tang B (2019) Quantitative model of irrigation effect on maize yield by deep neural network. Neural Comput Appl 1–14
Kang Y, Ozdogan M, Zhu X, Ye Z, Hain CR, Anderson MC (2020) Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest Environ Res Lett 1–23
Zhang L, Zhang Z, Luo Y, Cao J, Tao F (2020) Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in china using machine learning approaches. Remote Sens 12(1):21–48
Wang Y, Zhang Z, Feng L, Du Q, Runge T (2020) Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens 12(8):1232–1259
Ju S, Lim H, Heo J (2020) Machine learning approaches for crop yield prediction with MODIS and weather data. In: 40th Asian conference on remote sensing: progress of remote sensing technology for smart future, ACRS 2019
Yalcin H (2019) An approximation for a relative crop yield estimate from field images using deep learning. In: 8th international conference on agro-geoinformatics (Agro-Geoinformatics). IEEE, pp 1–6
Wang AX, Tran C, Desai N, Lobell D, Ermon S (2018) Deep transfer learning for crop yield prediction with remote sensing data. In: Proceedings of the 1st ACM SIGCAS conference on computing and sustainable societies, pp 1–5
Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, Singh AK (2021) Crop yield prediction integrating genotype and weather variables using deep learning. PLoS ONE 1371:1–19
Gomez D, Salvador P, Sanz J, Casanova JL (2021) Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agric For Meteorol 300(108317):1–21
Apolo OEA, Guanter MJ, Egea G, Raja P, Ruiz PM (2020) Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur J Agron 115(126030):1–34
Shekoofa A, Emam Y, Shekoufa N, Ebrahimi M, Ebrahimie E (2014) Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture. PLoS ONE 9(5):e97288
Kunapuli SS, Ayala R, Benavidez-Gutierrez G, Cruzatty CA, Cabrera A, Fernandez C, Maiguashca J (2015) Yield prediction for precision territorial management in maize using spectral data. In: Precision agriculture at the 10th European conference on precision agriculture, ECPA, pp 199–206
Ahamed ATMS, Mahmood NT, Hossain N, Kabir MT, Das K, Rahman F, Rahman RM (2015) Applying data mining techniques to predict the annual yield of major crops and recommend planting different crops in different districts in Bangladesh. In: IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD
Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65
Yudego MB, Rahlf J, Astrup R, Dimitriou I (2016) Spatial yield estimates of fast-growing willow plantations for energy based on climatic variables in northern Europe. GCB Bioenergy 8(6):1093–1105
Everingham Y, Sexton J, Skocaj D, Bamber IG (2016) Accurate prediction of sugarcane yield using a random forest algorithm. Agron Sustain Dev 36(2):1–19
Yingxue S, Huan X, Lijiao Y (2017) Support vector machine-based open crop model (SBOCM): case of rice production in China. Saudi J Biol Sci 24(3):537–547
Cheng H, Damerow L, Sun Y, Blanke M (2017) Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. J Imag 3(1):6–23
Ali I, Cawkwell F, Dwyer E, Green S (2017) Modeling managed grassland biomass estimation by using multitemporal remote sensing data—a machine learning approach. J Sel Top Appl Earth Obs Remote Sens 10(7):3254–3264
Kouadio L, Deo RC, Byrareddy V, Adamowski JF, Mushtaq S, Nguyen VP (2018) Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput Electron Agric 155:324–338
Bi L, Hu G (2021) A genetic algorithm-assisted deep learning approach for crop yield prediction. Soft Comput 25:10617–10628
Ghazvinei PT, Darvishi HH, Mosavi A, Yusof KW, Alizamir M, Shamshirband S, Chau K (2018) Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng Appl Comput Fluid Mech 12(1):738–749
Xu X, Gao P, Zhu X, Guo W, Ding J, Li C, Wu X (2019) Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province. China Ecol Indic 101:943–953
Ranjan AK, Parida BR (2019) Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India). Spatial Inf Res 1–19
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Shaikh, T.A., Mir, W.A., Rasool, T. et al. Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk. Arch Computat Methods Eng 29, 4557–4597 (2022). https://doi.org/10.1007/s11831-022-09761-4
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DOI: https://doi.org/10.1007/s11831-022-09761-4