Implementation of Artificial Intelligence, Machine Learning, and Internet of Things (IoT) in revolutionizing Agriculture: A review on recent trends and challenges
Abstract
Artificial Intelligence (AI) can revolutionize agriculture which impacts a country’s economy, employing more than 30% of the world’s population directly or indirectly. It can fulfill the needs of an ever-growing world’s population through automation. Traditional farmland practices like weeding, pesticide spraying, irrigation, monitoring soil nutritional and moisture status, etc. can be performed quicker using robots, sensors, drones, and algorithms. It reduces water wastage and pesticide overuse, maintains soil fertility, helps in reducing labor and enhances crop yield and productivity despite world problems. However, its penetration into agriculture is still in its infancy due to its uneconomical nature, lack of expertise and big data requirement for accuracy among others. This paper delves deeper into the various applications and impacts of AI in agriculture, new tools being used, challenges and future scope related to this field. Combined with Artificial Neural Network (ANN) models and Machine Learning (ML), along with Expert systems (ES) and Internet of Things (IoT), AI can do wonders in agriculture in the subsequent years to come.
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Patel, K. G., & Patil, M. S. (2022). Artificial intelligence in agriculture. International Journal for Research in Applied Science and Engineering Technology, 10(2), 624–627. https://doi.org/10.22214/ijraset.2022.40308
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Pawar, S. B. (2020). Artificial Intelligence in Agriculture. International Journal of Engineering Research & Technology, 8(5).
Paymode, A. S., & Malode, V. B. (2022). Transfer learning for multi-crop leaf disease image classification using convolutional neural network vgg. Artificial Intelligence in Agriculture, 6, 23–33. https://doi.org/10.1016/j.aiia.2021.12.002
Perez-Ortiz, M., Gutierrez, P. A., Pena, J. M., Torres-Sanchez, J., Lopez-Granados, F., & Hervas-Martinez, C. (2016). Machine learning paradigms for weed mapping via unmanned aerial vehicles. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2016.7849987
Pérez-Zavala, R., Torres-Torriti, M., Cheein, F. A., & Troni, G. (2018). A pattern recognition strategy for visual grape bunch detection in vineyards. Computers and Electronics in Agriculture, 151, 136–149. https://doi.org/10.1016/j.compag.2018.05.019
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Plant, R. E. (1989). An artificial intelligence based method for scheduling crop management actions. Agricultural Systems, 31(1), 127–155. https://doi.org/10.1016/0308-521X(89)90017-6
Plant, R. E., Horrocks, R. D., Grimes, D. W., & Zelinski, L. J. (1992). Calex/cotton: An integrated expert system application for irrigation scheduling. Transactions of the ASAE, 35(6), 1833–1838. https://doi.org/10.13031/2013.28803
Plantix. Plantix Analytics. https://plantix.net/en/analytics/
Porto, S. M. C., Arcidiacono, C., Anguzza, U., & Cascone, G. (2014). Development of an information system for the traceability of citrus-plant nursery chain related to the Italian National Service for Voluntary Certification. Agricultural Engineering International: CIGR Journal, 16(2), 208–216. https://cigrjournal.org/index.php/Ejounral/article/view/2707
in nigeria. International Journal of Advanced Research in Computer and Communication Engineering, 7(7), 42–47.
Orlandini, S., Massetti, L., & Marta, A. D. (2008). An agrometeorological approach for the simulation of Plasmopara viticola. Computers and Electronics in Agriculture, 64(2), 149–161. https://doi.org/10.1016/j.compag.2008.04.004
Pagliai, M., Vignozzi, N., & Pellegrini, S. (2004). Soil structure and the effect of management practices. Soil and Tillage Research, 79(2), 131–143. https://doi.org/10.1016/j.still.2004.07.002
Panpatte, Deepak. (2018). Artificial Intelligence in Agriculture: An Emerging Era of Research.
Park, D.-H., & Park, J.-W. (2011). Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention. Sensors, 11(4), 3640–3651. https://doi.org/10.3390/s110403640
Partel, V., Charan Kakarla, S., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture, 157, 339–350. https://doi.org/10.1016/j.compag.2018.12.048
Pasqual, G. M. (1994). Development of an expert system for the identification and control of weeds in wheat, triticale, barley and oat crops. Computers and Electronics in Agriculture, 10(2), 117–134. https://doi.org/10.1016/0168-1699(94)90016-7
Pasqual, G. M., & Mansfield, J. (1988). Development of a prototype expert system for identification and control of insect pests. Computers and Electronics in Agriculture, 2(4), 263–276. https://doi.org/10.1016/0168-1699(88)90002-6
Patel, K. G., & Patil, M. S. (2022). Artificial intelligence in agriculture. International Journal for Research in Applied Science and Engineering Technology, 10(2), 624–627. https://doi.org/10.22214/ijraset.2022.40308
Patil, S. S., & Thorat, S. A. (2016). Early detection of grapes diseases using machine learning and IoT. 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), 1–5. https://doi.org/10.1109/CCIP.2016.7802887
Patil, S. S., Dhandra, B. V., Angadi, U. B., Shankar, A. G., & Joshi, N. (2009). Web based Expert System for Diagnosis of MicroNutrients’ Deficiencies in Crops. Proceedings of the World Congress on Engineering and Computer Science 2009, San Francisco, USA.
Pawar, S. B. (2020). Artificial Intelligence in Agriculture. International Journal of Engineering Research & Technology, 8(5).
Paymode, A. S., & Malode, V. B. (2022). Transfer learning for multi-crop leaf disease image classification using convolutional neural network vgg. Artificial Intelligence in Agriculture, 6, 23–33. https://doi.org/10.1016/j.aiia.2021.12.002
Perez-Ortiz, M., Gutierrez, P. A., Pena, J. M., Torres-Sanchez, J., Lopez-Granados, F., & Hervas-Martinez, C. (2016). Machine learning paradigms for weed mapping via unmanned aerial vehicles. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2016.7849987
Pérez-Zavala, R., Torres-Torriti, M., Cheein, F. A., & Troni, G. (2018). A pattern recognition strategy for visual grape bunch detection in vineyards. Computers and Electronics in Agriculture, 151, 136–149. https://doi.org/10.1016/j.compag.2018.05.019
Peshin, R., Jayaratne, K. S. U., & Sharma, R. (2014). Ipm extension. In Integrated Pest Management (pp. 493–529). Elsevier. https://doi.org/10.1016/B978-0-12-398529-3.00026-9
Peskett, M. (2019). IBM’s instant AI Soil Analysis - the AgroPad. Food and Farming Technology. https://www.foodandfarmingtechnology.com/news/data-analytics/ibms-instant-ai-soil-analysis-the-agropad.html
Peters, D. P. C., McVey, D. S., Elias, E. H., Pelzel‐McCluskey, A. M., Derner, J. D., Burruss, N. D., et al. (2020). Big data–model integration and AI for vector‐borne disease prediction. Ecosphere, 11(6). https://doi.org/10.1002/ecs2.3157
Pilarski, T., Happold, M., Pangels, H., Ollis, M., Fitzpatrick, K., & Stentz, A. (2002). The Demeter System for Automated Harvesting. Autonomous Robots, 13(1), 9–20. https://doi.org/10.1023/A:1015622020131
Pinto, D. B., Castro, I., & Vicente, A. A. (2006). The use of TIC’s as a managing tool for traceability in the food industry. Food Research International, 39(7), 772–781. https://doi.org/10.1016/j.foodres.2006.01.015
Plant, R. E. (1989). An artificial intelligence based method for scheduling crop management actions. Agricultural Systems, 31(1), 127–155. https://doi.org/10.1016/0308-521X(89)90017-6
Plant, R. E., Horrocks, R. D., Grimes, D. W., & Zelinski, L. J. (1992). Calex/cotton: An integrated expert system application for irrigation scheduling. Transactions of the ASAE, 35(6), 1833–1838. https://doi.org/10.13031/2013.28803
Plantix. Plantix Analytics. https://plantix.net/en/analytics/
Porto, S. M. C., Arcidiacono, C., Anguzza, U., & Cascone, G. (2014). Development of an information system for the traceability of citrus-plant nursery chain related to the Italian National Service for Voluntary Certification. Agricultural Engineering International: CIGR Journal, 16(2), 208–216. https://cigrjournal.org/index.php/Ejounral/article/view/2707
in nigeria. International Journal of Advanced Research in Computer and Communication Engineering, 7(7), 42–47.
Orlandini, S., Massetti, L., & Marta, A. D. (2008). An agrometeorological approach for the simulation of Plasmopara viticola. Computers and Electronics in Agriculture, 64(2), 149–161. https://doi.org/10.1016/j.compag.2008.04.004
Pagliai, M., Vignozzi, N., & Pellegrini, S. (2004). Soil structure and the effect of management practices. Soil and Tillage Research, 79(2), 131–143. https://doi.org/10.1016/j.still.2004.07.002
Panpatte, Deepak. (2018). Artificial Intelligence in Agriculture: An Emerging Era of Research.
Park, D.-H., & Park, J.-W. (2011). Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention. Sensors, 11(4), 3640–3651. https://doi.org/10.3390/s110403640
Partel, V., Charan Kakarla, S., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture, 157, 339–350. https://doi.org/10.1016/j.compag.2018.12.048
Pasqual, G. M. (1994). Development of an expert system for the identification and control of weeds in wheat, triticale, barley and oat crops. Computers and Electronics in Agriculture, 10(2), 117–134. https://doi.org/10.1016/0168-1699(94)90016-7
Pasqual, G. M., & Mansfield, J. (1988). Development of a prototype expert system for identification and control of insect pests. Computers and Electronics in Agriculture, 2(4), 263–276. https://doi.org/10.1016/0168-1699(88)90002-6
Patel, K. G., & Patil, M. S. (2022). Artificial intelligence in agriculture. International Journal for Research in Applied Science and Engineering Technology, 10(2), 624–627. https://doi.org/10.22214/ijraset.2022.40308
Patil, S. S., & Thorat, S. A. (2016). Early detection of grapes diseases using machine learning and IoT. 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), 1–5. https://doi.org/10.1109/CCIP.2016.7802887
Patil, S. S., Dhandra, B. V., Angadi, U. B., Shankar, A. G., & Joshi, N. (2009). Web based Expert System for Diagnosis of MicroNutrients’ Deficiencies in Crops. Proceedings of the World Congress on Engineering and Computer Science 2009, San Francisco, USA.
Pawar, S. B. (2020). Artificial Intelligence in Agriculture. International Journal of Engineering Research & Technology, 8(5).
Paymode, A. S., & Malode, V. B. (2022). Transfer learning for multi-crop leaf disease image classification using convolutional neural network vgg. Artificial Intelligence in Agriculture, 6, 23–33. https://doi.org/10.1016/j.aiia.2021.12.002
Perez-Ortiz, M., Gutierrez, P. A., Pena, J. M., Torres-Sanchez, J., Lopez-Granados, F., & Hervas-Martinez, C. (2016). Machine learning paradigms for weed mapping via unmanned aerial vehicles. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2016.7849987
Pérez-Zavala, R., Torres-Torriti, M., Cheein, F. A., & Troni, G. (2018). A pattern recognition strategy for visual grape bunch detection in vineyards. Computers and Electronics in Agriculture, 151, 136–149. https://doi.org/10.1016/j.compag.2018.05.019
Peshin, R., Jayaratne, K. S. U., & Sharma, R. (2014). Ipm extension. In Integrated Pest Management (pp. 493–529). Elsevier. https://doi.org/10.1016/B978-0-12-398529-3.00026-9
Peskett, M. (2019). IBM’s instant AI Soil Analysis - the AgroPad. Food and Farming Technology. https://www.foodandfarmingtechnology.com/news/data-analytics/ibms-instant-ai-soil-analysis-the-agropad.html
Peters, D. P. C., McVey, D. S., Elias, E. H., Pelzel‐McCluskey, A. M., Derner, J. D., Burruss, N. D., et al. (2020). Big data–model integration and AI for vector‐borne disease prediction. Ecosphere, 11(6). https://doi.org/10.1002/ecs2.3157
Pilarski, T., Happold, M., Pangels, H., Ollis, M., Fitzpatrick, K., & Stentz, A. (2002). The Demeter System for Automated Harvesting. Autonomous Robots, 13(1), 9–20. https://doi.org/10.1023/A:1015622020131
Pinto, D. B., Castro, I., & Vicente, A. A. (2006). The use of TIC’s as a managing tool for traceability in the food industry. Food Research International, 39(7), 772–781. https://doi.org/10.1016/j.foodres.2006.01.015
Plant, R. E. (1989). An artificial intelligence based method for scheduling crop management actions. Agricultural Systems, 31(1), 127–155. https://doi.org/10.1016/0308-521X(89)90017-6
Plant, R. E., Horrocks, R. D., Grimes, D. W., & Zelinski, L. J. (1992). Calex/cotton: An integrated expert system application for irrigation scheduling. Transactions of the ASAE, 35(6), 1833–1838. https://doi.org/10.13031/2013.28803
Plantix. Plantix Analytics. https://plantix.net/en/analytics/
Porto, S. M. C., Arcidiacono, C., Anguzza, U., & Cascone, G. (2014). Development of an information system for the traceability of citrus-plant nursery chain related to the Italian National Service for Voluntary Certification. Agricultural Engineering International: CIGR Journal, 16(2), 208–216. https://cigrjournal.org/index.php/Ejounral/article/view/2707
in nigeria. International Journal of Advanced Research in Computer and Communication Engineering, 7(7), 42–47.
Orlandini, S., Massetti, L., & Marta, A. D. (2008). An agrometeorological approach for the simulation of Plasmopara viticola. Computers and Electronics in Agriculture, 64(2), 149–161. https://doi.org/10.1016/j.compag.2008.04.004
Pagliai, M., Vignozzi, N., & Pellegrini, S. (2004). Soil structure and the effect of management practices. Soil and Tillage Research, 79(2), 131–143. https://doi.org/10.1016/j.still.2004.07.002
Panpatte, Deepak. (2018). Artificial Intelligence in Agriculture: An Emerging Era of Research.
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