Artificial Intelligence in Agriculture: Techniques and Outcomes

Authors

  • DILEEP KUMAR GUPTA School of Engineering, Galgotias University, Greater Noida, India
  • SUNIL KUMAR School of Engineering, Galgotias University, Greater Noida, India
  • PRADEEP KUMAR Kamla Nehru Institute of Technology, Sultanpur (U.P.)
  • NITESH AWASTHI Department of Earth & Planetary Sciences, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
  • KAILAS K DAKHORE AICRP on Agrometeorology, VNMKV, Parbhani 431401, MS, India
  • SHRIKANT TIWARI School of Computer Science and Engineering, Galgotias University, Greater Noida, India
  • MD. SHAZLI AL HAQUE School of Engineering, Department of Mechanical Engineering, Galgotias University, Greater Noida, India

DOI:

https://doi.org/10.54386/jam.v28i1.3110

Keywords:

Artificial Intelligence, Precision Farming, Machine Learning, Deep Learning, Smart Farming

Abstract

Artificial Intelligence (AI) is emerging as a transformative driver of modern agriculture by enabling intelligent, data-driven solutions across crop production, soil and water management, climate forecasting, pest and disease detection, livestock monitoring, and supply chain optimization. The review article systematically addresses and provides answers to the five-research scope of purpose. This review establishes the relevance of AI to current agricultural needs by synthesizing how these technologies align with the demands of precision, sustainability, and resilience. The article highlights the agricultural parameters such as yield, soil health, water resources, and livestock well-being that are being effectively monitored and managed through AI applications. It examines key techniques including machine learning, deep learning, computer vision, and robotics, which underpin advancements in predictive analytics, automation, and decision support. The review evaluates measurable outcomes, including yield improvements, reduced chemical and water use, enhanced energy efficiency, and optimized post-harvest processes. Finally, the study identifies major challenges such as data heterogeneity, affordability barriers, digital literacy gaps, and ethical concerns, while also discussing future prospects for broader and equitable adoption. This review provides actionable insights for researchers, practitioners, education, extension and policymakers, contributing to the development of sustainable and resilient agricultural practices through AI by aligning its findings with this scope of purposes.

References

Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762. https://doi.org/10.1016/J.JAFR.2025.101762

Ali, F., Razzaq, A., Tariq, W., Hameed, A., Rehman, A., Razzaq, K., Sarfraz, S., Rajput, N. A., Zaki, H. E. M., Shahid, M. S., & Ondrasek, G. (2024). Spectral Intelligence: AI-Driven Hyperspectral Imaging for Agricultural and Ecosystem Applications. Agronomy 2024, Vol. 14, Page 2260, 14(10), 2260. https://doi.org/10.3390/AGRONOMY14102260

Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., Notarnicola, C., Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., & Notarnicola, C. (2015). Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sensing 2015, Vol. 7, Pages 16398-16421, 7(12), 16398–16421. https://doi.org/10.3390/RS71215841

Awasthi, N., Tripathi, J. N., Petropoulos, G. P., Gupta, D. K., Singh, A. K., & Kathwas, A. K. (2023). Performance assessment of Global-EO-based precipitation products against gridded rainfall from the Indian Meteorological Department. Remote Sensing, 15(13), 3443.

Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/J.COMPAG.2022.107119

Basu, A., & Narayan, A. (2025). The role of machine learning in transforming agricultural practices: insights into crop yield optimization and disease detection. Iran Journal of Computer Science, 1–19. https://doi.org/10.1007/S42044-025-00280-6/METRICS

Bhati, B. S., Rai, C. S., Balamurugan, B., & Al-Turjman, F. (2020). An intrusion detection scheme based on the ensemble of discriminant classifiers. Comput. Electr. Eng., 86(106742), 106742.

Buhler, D. D., Liebman, M., & Obrycki, J. J. (2000). Theoretical and practical challenges to an IPM approach to weed management. Weed Science, 48(3), 274–280. https://doi.org/10.1614/0043-1745(2000)048[0274:TAPCTA]2.0.CO;2

Chou, F. N.-F. (1988). Optimal Real-Time Pump and Irrigation Scheduling for Center-Pivot Sprinkler Systems. https://doi.org/10.2172/5425039

Dahiya, N., Singh, G., Gupta, D. K., Kalogeropoulos, K., Detsikas, S. E., Petropoulos, G. P., Singh, S., & Sood, V. (2024). A novel Deep Learning Change Detection approach for estimating Spatiotemporal Crop Field Variations from Sentinel-2 imagery. Remote Sensing Applications: Society and Environment, 101259.

Das, D., Singh, M., Mohanty, S. S., & Chakravarty, S. (2020). Leaf Disease Detection using Support Vector Machine. Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020, 1036–1040. https://doi.org/10.1109/ICCSP48568.2020.9182128

DeLay, N. D., Thompson, N. M., & Mintert, J. R. (2022). Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73(1), 195–219. https://doi.org/10.1111/1477-9552.12440;REQUESTEDJOURNAL:JOURNAL:14779552;WGROUP:STRING:PUBLICATION

Dhanya, V. G., Subeesh, A., Kushwaha, N. L., Vishwakarma, D. K., Nagesh Kumar, T., Ritika, G., & Singh, A. N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6, 211–229. https://doi.org/10.1016/J.AIIA.2022.09.007

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/J.RSE.2011.11.026

Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven Predictive Analytics In Agricultural Supply Chains: A Review: Assessing The Benefits And Challenges Of Ai In Forecasting Demand And Optimizing Supply In Agriculture. Computer Science & IT Research Journal, 5(2), 473–497. https://doi.org/10.51594/CSITRJ.V5I2.817

Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., … Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716. https://doi.org/10.1109/JPROC.2010.2043918

Fenu, G., Malloci, F. M., Fenu, G., & Malloci, F. M. (2021). Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms. Big Data and Cognitive Computing 2021, Vol. 5, 5(1), 1–24. https://doi.org/10.3390/BDCC5010002

Frelat, R., Lopez-Ridaura, S., Giller, K. E., Herrero, M., Douxchamps, S., Djurfeldt, A. A., Erenstein, O., Henderson, B., Kassie, M., Paul, B. K., Rigolot, C., Ritzema, R. S., Rodriguez, D., Van Asten, P. J. A., & Van Wijk, M. T. (2016). Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proceedings of the National Academy of Sciences of the United States of America, 113(2), 458–463. https://doi.org/10.1073/PNAS.1518384112;PAGE:STRING:ARTICLE/CHAPTER

Gupta, D., Gujre, N., Singha, S., & Mitra, S. (2022). Role of existing and emerging technologies in advancing climate-smart agriculture through modeling: A review. Ecological Informatics, 71, 101805. https://doi.org/10.1016/J.ECOINF.2022.101805

Gupta, D. K., Kumar, P., Mishra, V. N., & Prasad, R. (2014). Soil Moisture estimation by ANN using Bistatic Scatterometer data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 97–100.

Gupta, D. K., Prasad, R., Kumar, P., & Mishra, V. N. (2016). Estimation of crop variables using bistatic scatterometer data and artificial neural network trained by empirical models. Computers and Electronics in Agriculture, 123, 64–73.

Gupta, D. K., Prasad, R., Kumar, P., & Vishwakarma, A. K. (2017). Soil moisture retrieval using ground based bistatic scatterometer data at X-band. Advances in Space Research, 59(4), 996–1007.

Gupta, D. K., Prasad, R., Kumar, P., Vishwakarma, A. K., & Srivastava, P. K. (2018). Vegetation water content retrieval using scatterometer data at X-band. Geocarto International, 33(6), 602–611.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/QJ.3803;ISSUE:ISSUE:DOI

Hewage, P., Trovati, M., Pereira, E., & Behera, A. (2020). Deep learning-based effective fine-grained weather forecasting model. Pattern Analysis and Applications 2020 24:1, 24(1), 343–366. https://doi.org/10.1007/S10044-020-00898-1

Hoque, A., & Padhiary, M. (2024). Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability. Asian Journal of Research in Computer Science, 17(10), 95–109. https://doi.org/10.9734/ajrcos/2024/v17i10512

Huber, F., Yushchenko, A., Stratmann, B., & Steinhage, V. (2022). Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches. Computers and Electronics in Agriculture, 202, 107346. https://doi.org/10.1016/J.COMPAG.2022.107346

Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2

John, S., & Arul Leena Rose, P. J. (2024). Smart Farming and Precision Agriculture and Its Need in Today’s World. Signals and Communication Technology, Part F2461, 19–44. https://doi.org/10.1007/978-3-031-51195-0_2

Jones, P. G., & Thornton, P. K. (2013). Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agricultural Systems, 114, 1–5. https://doi.org/10.1016/J.AGSY.2012.08.002

Kumar, K., Varshney, L., Ambikapathy, A., Mittal, V., Prakash, S., Chandra, P., & Khan, N. (2021a). Soft computing and IoT based solar tracker. Int. J. Power Electron. Drive Syst. (IJPEDS), 12(3), 1880.

Kumar, M., Shenbagaraman, V. M., Shaw, R. N., & Ghosh, A. (2021b). Predictive data analysis for energy management of a smart factory leading to sustainability. In Lecture Notes in Electrical Engineering (pp. 765–773). Springer Singapore.

Kumar, R., Farooq, M., & Qureshi, M. (2024). Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation. A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences, 151–165. https://doi.org/10.1016/B978-0-443-24001-0.00010-5

Kumar, V., Sharma, K. V., Caloiero, T., Mehta, D. J., & Singh, K. (2023). Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances. Hydrology 2023, Vol. 10, Page 141, 10(7), 141. https://doi.org/10.3390/HYDROLOGY10070141

Laskar, A. A. (2024). Exploring the Role of Smart Systems in Farm Machinery for Soil Fertility and Crop Productivity. International Journal For Science Technology And Engineering, 12(12), 2063–2075. https://doi.org/10.22214/IJRASET.2024.66157

Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74(1), 2–33. https://doi.org/10.1016/J.COMPAG.2010.08.005

Li, L., Zhang, Q., Huang, D., Li, L., Zhang, Q., & Huang, D. (2014). A Review of Imaging Techniques for Plant Phenotyping. Sensors 2014, Vol. 14, Pages 20078-20111, 14(11), 20078–20111. https://doi.org/10.3390/S141120078

Li, L., Zhang, S., & Wang, B. (2021). Plant Disease Detection and Classification by Deep Learning - A Review. IEEE Access, 9, 56683–56698. https://doi.org/10.1109/ACCESS.2021.3069646

Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon, W. P., & Naylor, R. L. (2008). Prioritizing climate change adaptation needs for food security in 2030. Science, 319(5863), 607–610. https://doi.org/10.1126/SCIENCE.1152339;REQUESTEDJOURNAL:JOURNAL:SCIENCE;CTYPE:STRING:JOURNAL

Maharjan, S., Li, W., Fazli, S., Tariq, A., Thomas, R., Rakovski, C., & El-Askary, H. (2025). Enhancing water scarcity resilience in Egypt through machine learning-driven phenological crop mapping and water use efficiency analysis. International Journal of Applied Earth Observation and Geoinformation, 141(May 2024), 104668. https://doi.org/10.1016/J.JAG.2025.104668

Mathivanan, S. K., Sonaimuthu, S., Murugesan, S., Rajadurai, H., Shivahare, B. D., & Shah, M. A. (2024). Employing deep learning and transfer learning for accurate brain tumor detection. Sci. Rep., 14(1), 7232.

Mohammed, M. E. A., & Munir, M. (2025). Towards smart farming: applications of artificial intelligence and internet of things in precision agriculture. Hyperautomation in Precision Agriculture: Advancements and Opportunities for Sustainable Farming, 27–37. https://doi.org/10.1016/B978-0-443-24139-0.00003-5

Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66–84. https://doi.org/10.1016/J.COMPAG.2015.08.011

Onyeaka, H., Tamasiga, P., Nwauzoma, U. M., Miri, T., Juliet, U. C., Nwaiwu, O., & Akinsemolu, A. A. (2023). Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability 2023, Vol. 15, Page 10482, 15(13), 10482. https://doi.org/10.3390/SU151310482

Pandey, D. K., & Mishra, R. (2024). Towards sustainable agriculture: Harnessing AI for global food security. Artificial Intelligence in Agriculture, 12, 72–84. https://doi.org/10.1016/J.AIIA.2024.04.003

Panotra, N., Deepika, R. B., Roy, P., Singh, Y., Sinha, S., Choudhary, N., Choudhary, B., Gangwar, A., Mohanty, L. K., & Gopal, R. (2025). Advances in Precision Agriculture: A Review of Technologies, Applications and Future Prospects. Archives of Current Research International, 25(8), 722–737. https://doi.org/10.9734/ACRI/2025/V25I81454

Patel, R. M., & Bunkar, K. (2025). Soybean yield prediction leveraging advanced ensemble machine learning models. Journal of Agrometeorology, 27(2), 227–229. https://doi.org/10.54386/JAM.V27I2.2971

Poongodi, T., Krishnamurthi, R., Indrakumari R and Suresh, P., & Balusamy, B. (2020). Wearable Devices and IoT. In A Handbook of Internet of Things in Biomedical and Cyber Physical System (pp. 245–273). Springer International Publishing.

Prasad, A., Singh, R. K., Ramana Rao, K. V., & Saxena, C. K. (2025). Applicability of machine learning models for drought prediction using SPI in Kalahandi, Odisha. Journal of Agrometeorology, 27(2), 216–220. https://doi.org/10.54386/JAM.V27I2.2906

Punia, S. K., Kumar, M., Stephan, T., Deverajan, G. G., & Patan, R. (2021). Performance analysis of machine learning algorithms for big data classification. Int. J. E-health Med. Commun., 12(4), 60–75.

Rai, S., Nandre, J., & Kanawade, B. R. (2022). A Comparative Analysis of Crop Yield Prediction using Regression. 2022 2nd International Conference on Intelligent Technologies, CONIT 2022. https://doi.org/10.1109/CONIT55038.2022.9847783

Rajasekar, V., Vaishnnave, M. P., Premkumar, S., Sarveshwaran, V., & Rangaraaj, V. (2023). Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques. Results Eng., 18(101111), 101111.

Rana, T., Shankar, A., Sultan Mohd Kamran and Patan, R., & Balusamy, B. (2019, January). An intelligent approach for UAV and drone privacy security using blockchain methodology. 2019 9th International Conference on Cloud Computing, Data & Engineering (Confluence).

Raymond Hunt, E., Daughtry, C. S. T., Eitel, J. U. H., & Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 103(4), 1090–1099. https://doi.org/10.2134/AGRONJ2010.0395;PAGEGROUP:STRING:PUBLICATION

Roy, D. P., Wulder, M. A., Loveland, T. R., C.E., W., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., … Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/J.RSE.2014.02.001

Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., Siegwart, R., Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018). WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming. Remote Sensing 2018, Vol. 10, 10(9). https://doi.org/10.3390/RS10091423

Saagu Baagu. (2023). EoI For Saagu Baagu 2.0 Request for Expression of Interest For “Saagu Baagu” Project 2.0 (Partnership in Scale up) Department of Agriculture Government of Telangana State. https://it.telangana.gov.in/wp-content/uploads/2024/02/Telangana-Saagu-Baagu-2.0.pdf

Sakthipriya, D., & Chandrakumar, T. (2024). Weather based paddy yield prediction using machine learning regression algorithms. Journal of Agrometeorology, 26(3), 344–348. https://doi.org/10.54386/JAM.V26I3.2598

Shahane, A. A., & Shivay, Y. S. (2021). Soil Health and Its Improvement Through Novel Agronomic and Innovative Approaches. Frontiers in Agronomy, 3, 680456. https://doi.org/10.3389/FAGRO.2021.680456/XML/NLM

Shams, M. Y., Gamel, S. A., & Talaat, F. M. (2024). Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making. Neural Computing and Applications, 36(11), 5695–5714. https://doi.org/10.1007/S00521-023-09391-2/FIGURES/9

Sharma, D., Devi, M. C., Veeraiah, V., Kasar, M., Aggarwal, D., & Sharma, T. (2024). AI-Driven Precision Agriculture: Techniques for Monitoring Crop Health and Yield Optimization. Proceedings - 4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024, 1794–1800. https://doi.org/10.1109/ICTACS62700.2024.10840749

Sharma, G., Shrestha, S., Kunwar, S., & Tseng, T. M. (2021). Crop Diversification for Improved Weed Management: A Review. Agriculture 2021, Vol. 11, Page 461, 11(5), 461. https://doi.org/10.3390/AGRICULTURE11050461

Sharma, K., & Shivandu, S. K. (2024). Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors International, 5, 100292. https://doi.org/10.1016/J.SINTL.2024.100292

Sharma, Y., & Balamurugan, B. (2020). Preserving the privacy of electronic health records using blockchain. Procedia Comput. Sci., 173, 171–180.

Singh, G., & Goel, A. K. (2020, March). Face detection and recognition system using digital image processing. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

Singh, K., Yadav, M., Barak, D., Bansal, S., & Moreira, F. (2025). Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting. Sustainability 2025, Vol. 17, Page 4711, 17(10), 4711. https://doi.org/10.3390/SU17104711

Siva, K. P., Shri Suriya, P., Praveen, R., Nimesh Rajan, D., & Santhosh Kumar, G. (2024). Crop Productivity Enhancement Through Data Analytics and IOT. 10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024, 2016–2020. https://doi.org/10.1109/ICACCS60874.2024.10717032

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0

Umamaheswari, S., Vishal, N. R., Pragadesh, N. R., & Lavanya, S. (2023). Performance Analysis of ResNet50 Architecture based Pest Detection System. 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, 578–583. https://doi.org/10.1109/ICACCS57279.2023.10112802

Upadhyay, N., & Bhargava, A. (2025). Artificial intelligence in agriculture: applications, approaches, and adversities across pre-harvesting, harvesting, and post-harvesting phases. Iran Journal of Computer Science, 1–24. https://doi.org/10.1007/S42044-025-00264-6/METRICS

Vaidheki, M., Gupta, D. S., Basak, P., Debnath, M. K., Hembram, S., & Ajith, S. (2023). Prediction of potato late blight disease incidence based on weather variables using statistical and machine learning models: A case study from West Bengal. Journal of Agrometeorology, 25(4), 583–588. https://doi.org/10.54386/JAM.V25I4.2272

Virnodkar, S. S., Pachghare, V. K., Patil, V. C., & Jha, S. K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture, 21(5), 1121–1155. https://doi.org/10.1007/S11119-020-09711-9/METRICS

Yashabh, Naman, Fayaz, A., & Kaundal, M. (2025). Leveraging Mobile Technology to Enhance Agricultural Extension Services: A Review. Archives of Current Research International, 25(7), 804–817. https://doi.org/10.9734/ACRI/2025/V25I71380

Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 2012 13:6, 13(6), 693–712. https://doi.org/10.1007/S11119-012-9274-5

Zhou, H., Wang, X., Au, W., Kang, H., & Chen, C. (2022). Intelligent robots for fruit harvesting: recent developments and future challenges. Precision Agriculture, 23(5), 1856–1907. https://doi.org/10.1007/S11119-022-09913-3/FIGURES/7

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01-03-2026

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GUPTA, D. K., SUNIL KUMAR, PRADEEP KUMAR, AWASTHI, N., KAILAS K DAKHORE, SHRIKANT TIWARI, & MD. SHAZLI AL HAQUE. (2026). Artificial Intelligence in Agriculture: Techniques and Outcomes. Journal of Agrometeorology, 28(1), 114–125. https://doi.org/10.54386/jam.v28i1.3110

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Vol. 28 No. 1 (2026): March

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