Robotics and Artificial Intelligence in Today's Agriculture
DOI:
https://doi.org/10.14738/aivp.126.17824Keywords:
Agriculture, Artificial Intelligence, Machine learning, RoboticsAbstract
The increasing population poses an ever-increasing demand for food production amidst major constraints like decreasing agricultural labour, increased growth rate of industrialization, urbanization, reduced land and water availability for agriculture, and drudgery in farm works. With rapid development of technology in the field of robotics and AI, has created new horizons for its application in agriculture and allied sectors. These latest technologies help farmers in facing the challenges in food production to ensure food security, environmental sustainability and labour efficiency in the age-old industry. In this review article, a comprehensive view of the current state and future trends of robotics and AI in various agricultural domains, like crop monitoring, weed control, harvesting, sorting and transportation are discussed.
References
Zhang, P., et al., Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants, 2021. 7(7): p. 864-876.
Vijayakumar, V. and N. Balakrishnan, RETRACTED ARTICLE: Artificial intelligence-based agriculture automated monitoring systems using WSN. Journal of Ambient Intelligence and Humanized Computing, 2021. 12(7): p. 8009-8016.
Javaid, M., et al., Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 2022. 7(01): p. 83-111.
Abdullahi, H.S., F. Mahieddine, and R.E. Sheriff. Technology impact on agricultural productivity: A review of precision agriculture using unmanned aerial vehicles. in Wireless and Satellite Systems: 7th International Conference, WiSATS 2015, Bradford, UK, July 6-7, 2015. Revised Selected Papers 7. 2015. Springer.
Subeesh, A. and C. Mehta, Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture, 2021. 5: p. 278-291.
McKinion, J. and H. Lemmon, Expert systems for agriculture. Computers and Electronics in Agriculture, 1985. 1(1): p. 31-40.
Roach, J., et al., An expert system for helping apple growers. Computers and electronics in agriculture, 1987. 2(2): p. 97-108.
Stone, N.D. and T.W. Toman, A dynamically linked expert-database system for decision support in Texas cotton production. Computers and electronics in agriculture, 1989. 4(2): p. 139-148.
Lemmon, H., COMAX: An expert system for cotton crop management. Science, 1986. 233(4759): p. 29-33.
Robinson, C. and N. Mort, A neural network system for the protection of citrus crops from frost damage. Computers and electronics in agriculture, 1997. 16(3): p. 177-187.
Li ShaoKun, L.S., et al., The machine recognition for population feature of wheat images based on BP neural network. 2002.
Mandow, A., et al., The autonomous mobile robot AURORA for greenhouse operation. IEEE Robotics & Automation Magazine, 1996. 3(4): p. 18-28.
Bechar, A. and C. Vigneault, Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 2016. 149: p. 94-111.
Bak, T. and H. Jakobsen, Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering, 2004. 87(2): p. 125-136.
Popa, C., Adoption of artificial intelligence in agriculture. 2011.
Streich, J., et al., Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals? Current opinion in biotechnology, 2020. 61: p. 217-225.
Aitkenhead, M., et al., A novel method for training neural networks for time-series prediction in environmental systems. Ecological Modelling, 2003. 162(1-2): p. 87-95.
Shadrin, D., et al., Enabling precision agriculture through embedded sensing with artificial intelligence. IEEE Transactions on Instrumentation and Measurement, 2019. 69(7): p. 4103-4113.
Linaza, M.T., et al., Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy, 2021. 11(6): p. 1227.
Mohr, S. and R. Kühl, Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 2021. 22(6): p. 1816-1844. URL: http://dx.doi.org/10.14738/aivp.1206.17824 129
Duckett, T., et al., Agricultural robotics: the future of robotic agriculture. arXiv preprint arXiv:1806.06762, 2018.
Kumar, R., et al., Artificial intelligence: new technology to improve Indian agriculture. International Journal of Chemical Studies, 2020. 8(2): p. 2999-3005.
Patrício, D.I. and R. Rieder, Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and electronics in agriculture, 2018. 153: p. 69-81.
Sabrina, F., et al., An interpretable artificial intelligence based smart agriculture system. Computers, Materials & Continua, 2022: p. 3777-3797.
Awasthi, Y., Press “a” for artificial intelligence in agriculture: A review. JOIV: International Journal on Informatics Visualization, 2020. 4(3): p. 112-116.
Skvortsov, E., Prospects of applying artificial intelligence technologies in the regional agriculture. Ekonomika Regiona= Economy of Regions, 2020(2): p. 563.
Perea, R.G., et al., Optimisation of water demand forecasting by artificial intelligence with short data sets. Biosystems engineering, 2019. 177: p. 59-66.
Choudhary, S., et al., Autonomous crop irrigation system using artificial intelligence. Int. J. Eng. Adv. Technol, 2019. 8(5): p. 46-51.
Spanaki, K., et al., Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning & Control, 2022. 33(16): p. 1498-1516.
Wu, S., et al., Long range wide area network for agricultural wireless underground sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2023. 14(5): p. 4903-4919.
Drury, B., et al., A survey of the applications of Bayesian networks in agriculture. Engineering Applications of Artificial Intelligence, 2017. 65: p. 29-42.
Swetha, D.N. and S. Balaji. Agriculture cloud system based emphatic data analysis and crop yield prediction using hybrid artificial intelligence. in Journal of Physics: Conference Series. 2021. IOP Publishing.
Kugler, L., Artificial intelligence, machine learning, and the fight against world hunger. Communications of the ACM, 2022. 65(2): p. 17-19.
Bhardwaj, H., et al., Artificial intelligence and its applications in agriculture with the future of smart agriculture techniques, in Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture. 2021, IGI Global. p. 25-39.
Marković, D., et al., Application of IoT in monitoring and controlling agricultural production. Acta Agriculturae Serbica, 2015. 20(40): p. 145-153.
Arvind, G., et al. Automated irrigation with advanced seed germination and pest control. in 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). 2017. IEEE.
Jha, K., et al., A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2019. 2: p. 1-12.
Indolia, S., et al., Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science, 2018. 132: p. 679-688.
Anthony, D., et al. On crop height estimation with UAVs. in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2014. IEEE.
Liu, G., et al. An artificial neural network-based expert system for fruit tree disease and insect pest diagnosis. in 2006 IEEE International Conference on Networking, Sensing and Control. 2006. IEEE.
Ehret, D.L., et al., Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Computers and electronics in agriculture, 2011. 79(1): p. 82-89.
Mokaya, V., Future of precision agriculture in India using machine learning and artificial intelligence. International Journal of Computer Sciences and Engineering, 2019. 7(2): p. 1020-1023.
Jose, A., S. Nandagopalan, and C.M.V.S. Akana, Artificial Intelligence techniques for agriculture revolution: a survey. Annals of the Romanian Society for Cell Biology, 2021: p. 2580–2597-2580–2597.
Kumar, T.A., et al., Intelligent food and grain storage management system for the warehouse and cold storage. Int. J. Res. Eng. Sci. Manag, 2018. 1(4): p. 130-132.
Grogan, J., et al., Microcomputer-based tractor performance monitoring and optimization system. Journal of Agricultural Engineering Research, 1987. 38(4): p. 227-243.
Yahya, A., et al., Mapping system for tractor-implement performance. Computers and Electronics in Agriculture, 2009. 69(1): p. 2-11.
Civele, C., Development of an IOT based tractor tracking device to be used as a precision agriculture tool for Turkey’s agricultural tractors. Sch. J. Agric. Vet. Sci, 2019. 6: p. 199-203.
Dozono, K., S. Amalathas, and R. Saravanan. The impact of cloud computing and artificial intelligence in digital agriculture. in Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 1. 2022. Springer.
Song, H. and Y. He. Crop nutrition diagnosis expert system based on artificial neural networks. in Third International Conference on Information Technology and Applications (ICITA'05). 2005. IEEE.
Papageorgiou, E.I., A.T. Markinos, and T.A. Gemtos, Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Applied Soft Computing, 2011. 11(4): p. 3643-3657.
Eli-Chukwu, N.C., Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 2019. 9(4).
Elijah, O., et al., An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of things Journal, 2018. 5(5): p. 3758-3773.
Montas, H. and C. Madramootoo, A decision support system for soil conservation planning. Computers and electronics in agriculture, 1992. 7(3): p. 187-202.
López, E.M., et al., A fuzzy expert system for soil characterization. Environment international, 2008. 34(7): p. 950-958.
Levine, E., D. Kimes, and V. Sigillito, Classifying soil structure using neural networks. Ecological modelling, 1996. 92(1): p. 101-108.
Chang, D.-H. and S. Islam, Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, 2000. 74(3): p. 534-544. 131
Bilgili, M., The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey. Turkish Journal of Agriculture and Forestry, 2011. 35(1): p. 83-93.
Elshorbagy, A. and K. Parasuraman, On the relevance of using artificial neural networks for estimating soil moisture content. Journal of Hydrology, 2008. 362(1-2): p. 1-18.
Zhao, Z., et al., Predict soil texture distributions using an artificial neural network model. Computers and electronics in agriculture, 2009. 65(1): p. 36-48.
Li, M. and R. Yost, Management-oriented modeling: optimizing nitrogen management with artificial intelligence. Agricultural Systems, 2000. 65(1): p. 1-27.
Al-Ali, A., et al., IoT-solar energy powered smart farm irrigation system. Journal of Electronic Science and Technology, 2019. 17(4): p. 100017.
Singh, A., et al., Geoinformatics, artificial intelligence, sensor technology, big data: emerging modern tools for sustainable agriculture. Sustainable Agriculture Systems and Technologies, 2022: p. 295-313.
Valdés-Vela, M., et al., Soft computing applied to stem water potential estimation: A fuzzy rule based approach. Computers and Electronics in Agriculture, 2015. 115: p. 150-160.
Arif, C., M. Mizoguchi, and B.I. Setiawan, Estimation of soil moisture in paddy field using artificial neural networks. arXiv preprint arXiv:1303.1868, 2013.
Lavanya, G., C. Rani, and P. GaneshKumar, An automated low cost IoT based Fertilizer Intimation System for smart agriculture. Sustainable Computing: Informatics and Systems, 2020. 28: p. 100300.
Khan, R., et al., Machine learning-based agriculture, in Application of Machine Learning in Agriculture. 2022, Elsevier. p. 3-27.
Analysis, U.B.o.E., Value added by industry as a percentage of gross domestic product. 2017.
Balleda, K., et al. Agpest: An efficient rule-based expert system to prevent pest diseases of rice & wheat crops. in 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO). 2014. IEEE.
Munirah, M. and M. Rozlini. An expert system development: its application on diagnosing oyster mushroom diseases. in 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013). 2013. IEEE.
Jesus, J., T. Panagopoulos, and A. Neves. Fuzzy logic and geographic information systems for pest control in olive culture. in 4th IASME/WSEAS International Conference on Energy, Environment, Ecosystems & Sustainable Development, Algarve, Portugal. 2008.
Kolhe, S., et al., A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops. Computers and electronics in agriculture, 2011. 76(1): p. 16-27.
Siraj, F. and N. Arbaiy, Integrated pest management system using fuzzy expert system. 2006.
Virparia, P., A Web Based Fuzzy Expert System for Insect Pest Management in Groundnut Crop ‘Prajna’. Journal Of Pure & Applied Sciences, 2007. 15: p. 36-41.
Partel, V., S.C. Kakarla, and Y. Ampatzidis, Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and electronics in agriculture,
Burks, T., et al., Evaluation of neural-network classifiers for weed species discrimination. Biosystems Engineering, 2005. 91(3): p. 293-304.
Shi, Y., et al. Analysis and testing of weed real-time identification based on neural network. in Computer And Computing Technologies In Agriculture, Volume II: First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, August 18-20, 2007 1. 2008. Springer.
Akbarzadeh, S., et al., Plant discrimination by Support Vector Machine classifier based on spectral reflectance. Computers and electronics in agriculture, 2018. 148: p. 250-258.
Katiyar, S., The use of pesticide management using artificial intelligence, in Artificial Intelligence Applications in Agriculture and Food Quality Improvement. 2022, IGI Global. p. 74-94.
Ghosh, I. and R. Samanta, TEAPEST: An expert system for insect pest management in tea. Applied Engineering in Agriculture, 2003. 19(5): p. 619.
Cai, J., et al., An early warning model for vegetable pests based on multidimensional data. Computers and Electronics in Agriculture, 2019. 156: p. 217-226.
Stigliani, L. and C. Resina, SELOMA: expert system for weed management in herbicide-intensive crops. Weed Technology, 1993. 7(3): p. 550-559.
Brazeau, M., Fighting weeds: can we reduce, or even eliminate, herbicides by utilizing robotics and AI. Genetic Literacy Project, North Wales, 2018.
Gerhards, R. and S. Christensen, Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed research, 2003. 43(6): p. 385-392.
Karimi, Y., et al., Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and electronics in agriculture, 2006. 51(1-2): p. 99-109.
Yang, C.C., et al., Development of neural networks for weed recognition in corn fields. Transactions of the ASAE, 2002. 45(3): p. 859.
De Oca, A.M., et al. Low-cost multispectral imaging system for crop monitoring. in 2018 International Conference on Unmanned Aircraft Systems (ICUAS). 2018. IEEE.
Chung, S.-O., et al., Sensing technologies for grain crop yield monitoring systems: A review. 바이오시스템공학 (구 한국농업기계학회지), 2016. 41(4): p. 408-417.
Baeten, J., et al. Autonomous fruit picking machine: A robotic apple harvester. in Field and Service Robotics: Results of the 6th International Conference. 2008. Springer.
Gutierrez-Galan, D., et al., Embedded neural network for real-time animal behavior classification. Neurocomputing, 2018. 272: p. 17-26.
Costa, F.G., et al. The use of unmanned aerial vehicles and wireless sensor network in agricultural applications. in 2012 IEEE International Geoscience and Remote Sensing Symposium. 2012. IEEE.
Williams, L.R., et al., A sensor-based solution to monitor grazing cattle drinking behaviour and water intake. Computers and electronics in agriculture, 2020. 168: p. 105141.
Benaissa, S., et al., Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors. Computers and electronics in agriculture, 2020. 168: p. 105153.
Astill, J., et al., Smart poultry management: Smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, 2020. 170: p. 105291.
Zuidhof, M., et al., Precision feeding: Innovative management of broiler breeder feed intake and flock uniformity. Poultry science, 2017. 96(7): p. 2254-2263.
Gambhire, A. and B.N. Shaikh Mohammad. Use of artificial intelligence in agriculture. in Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST). 2020.
Aubry, C., F. Papy, and A. Capillon, Modelling decision-making processes for annual crop management. Agricultural systems, 1998. 56(1): p. 45-65.
Franzen, D., Site Specific Farming 4: Economics and the Environment. 2009.
Johan From, P., et al., Rasberry-robotic and autonomous systems for berry production. Mechanical Engineering, 2018. 140(06): p. S14-S18.
Cruz, G.B.D. and J. Rubi, The Effects of AI in Various Spheres of Life.
Jyoti, B., N. Chandel, and K. Agrawal. Application of robotics in agriculture: an indian perspective. in Proceedings of the 8th Asian-Australasian Conference on Precision Agriculture. 2020.
El Hoummaidi, L., A. Larabi, and K. Alam, Using unmanned aerial systems and deep learning for agriculture mapping in Dubai. Heliyon, 2021. 7(10).
Ali, M.Y., S.B. Naeem, and R. Bhatti, Artificial Intelligence (AI) in Pakistani university library services. Library Hi Tech News, 2021. 38(8): p. 12-15.
Pilarski, T., et al., The demeter system for automated harvesting. Autonomous Robots, 2002. 13: p. 9-20.
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