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@article{198384,
        author = {UMESH KUMAR and PIYUSH BISEN and MANPREET SINGH and SHIVANSH TRIVEDI},
        title = {AGRICULTURE ON THE BASIS OF MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9131-9133},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198384},
        abstract = {Agriculture remains the backbone of global food security, yet it faces unprecedented challenges including climate change, population growth, resource scarcity, and the need for sustainable practices. Machine learning (ML) has emerged as a transformative technology in modern agriculture, enabling data-driven decision-making across the entire farming lifecycle. This paper presents a comprehensive review of ML applications in agriculture, encompassing crop yield prediction, disease and pest detection, soil health monitoring, precision irrigation, supply chain optimization, and autonomous farming systems. We analyze over 150 recent studies (2018–2024), examine the algorithmic approaches employed—from classical supervised learning to deep neural networks and reinforcement learning—and critically assess the challenges of data scarcity, model interpretability, and deployment in resource-limited environments. Our findings indicate that convolutional neural networks (CNNs) and ensemble methods dominate current applications, while federated learning and edge AI represent promising emerging frontiers. We conclude with a research agenda for achieving climate-resilient, data-driven agriculture.},
        keywords = {},
        month = {April},
        }