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.

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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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