A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture

Year : 2026 | Volume : 15 | Issue : 01 | Page : 39 45
    By

    Arvinder Kaur,

  • Shalu Gupta,

  • Nomsa C. C. Kamgwira,

  1. Student, Department of Computer Applications, Guru Kashi University, Talwandi Sabo, Punjab, India
  2. Associate Professor, Department of Computer Applications, Guru Kashi University, Talwandi Sabo, Punjab, India
  3. Student, Department of Computer Applications, Guru Kashi University, Talwandi Sabo, Punjab, India

Abstract

Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, yield forecasting, and livestock management. We discuss commonly used ML paradigms including supervised, unsupervised learning, and deep learning along with specific algorithms such as Support Vector Machines, Random Forests, and Convolutional Neural Networks. Recent advancements, practical implementations, major challenges (e.g., data scarcity, infrastructure limitations, and adoption barriers), and future research directions are critically analyzed. This review highlights how ML is contributing to sustainable, efficient, and resilient agricultural systems. Furthermore, the adoption of machine learning in agriculture has significant implications for enhancing climate resilience and supporting decision-making at both farm and policy levels. By enabling real-time analysis and predictive insights, ML-driven tools can assist farmers in adapting to climate variability, reducing production risks, and minimizing environmental impacts through precise input management. Despite promising outcomes, the scalability and accessibility of these technologies remain uneven, particularly in developing regions where limited digital infrastructure and technical capacity pose major constraints. Therefore, collaborative efforts among researchers, technology developers, policymakers, and extension services are essential to bridge existing gaps and promote inclusive digital agriculture. Continued research focusing on explainable AI, low-cost sensing technologies, and farmer-centric ML solutions will be crucial for achieving long-term sustainability and global food security

Keywords: Crop disease detection, deep learning, IoT in agriculture, machine learning, precision agriculture, sustainable farming, yield prediction

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Arvinder Kaur, Shalu Gupta, Nomsa C. C. Kamgwira. A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture. Research & Reviews : Journal of Agricultural Science and Technology. 2026; 15(01):39-45.
How to cite this URL:
Arvinder Kaur, Shalu Gupta, Nomsa C. C. Kamgwira. A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture. Research & Reviews : Journal of Agricultural Science and Technology. 2026; 15(01):39-45. Available from: https://journals.stmjournals.com/rrjoast/article=2026/view=236732


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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 20/12/2025
Accepted 15/01/2026
Published 11/02/2026
Publication Time 53 Days
108

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