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@article{186386,
        author = {Silambarasan G and Dr. I. Laurence Aroquiaraj},
        title = {Advancing Agro-Technology: A Comprehensive Assessment of Artificial Intelligence Methodologies for Optimizing the Physicochemical and Functional Attributes of Agricultural Commodities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1248-1270},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186386},
        abstract = {The integration of Artificial Intelligence (AI) within agricultural systems signifies a paradigm shift toward data-driven precision and operational efficiency. Convolutional Neural Networks (CNNs) facilitate early-stage detection of Phyto pathological anomalies through image-based classification frameworks, thereby mitigating yield degradation. Long Short-Term Memory (LSTM) architectures enhance predictive analytics for yield estimation and soil fertility dynamics, contributing to optimized resource distribution. Although the global agricultural sector continues to grapple with mechanization and automation constraints, contemporary AI and machine learning (ML) paradigms have redefined agrotechnological methodologies This review synthesizes advancements in AI-enabled systems encompassing ML algorithms, deep learning (DL) architectures, Internet of Things (IoT) frameworks, and Decision Support Systems (DSS), underscoring their contribution to challenges in yield optimization, precision irrigation management, pest diagnostics, and strategic decision-making. Furthermore, it delineates AI-driven innovations in domains such as genomic-assisted plant breeding, smart irrigation networks, supply chain logistics, and post-harvest packaging optimization. Despite substantial progress, large-scale implementation remains hindered by economic constraints, data governance issues, infrastructural deficits, and insufficient digital proficiency. The review provides a critical evaluation of both the transformative potential and the prevailing limitations of AI in contemporary agriculture.},
        keywords = {},
        month = {November},
        }