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@article{208088,
        author = {DR. NIRAJ SUNIL BHARAMBE and PARIKSHIT BHAGAT and MAKARAND KADAM and SANAJAN RAMESH BHANGALE},
        title = {THE USE OF ARTIFICIAL INTELLIGENCE IN MODERN FOOD PRODUCTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {286-292},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208088},
        abstract = {Food systems everywhere are being squeezed from several directions at once: a population that keeps climbing, weather that no longer behaves the way historical records suggest it should, soils that are quietly losing fertility, and supply chains that have grown too complex to manage on spreadsheets and instinct alone. Artificial intelligence (AI) has become one of the more consequential responses to this squeeze, not because it replaces good agronomy or sound engineering, but because it lets producers, processors, and distributors act on patterns in data that would otherwise go unnoticed until it was too late to matter. This paper looks at how AI technologies—machine learning, computer vision, natural language processing, robotics, and the Internet of Things (IoT)—are being put to use across five stages of the food production value chain: planning and farm inputs, crop and livestock production, harvesting and processing, packaging and distribution, and retail. Drawing on recent industry and academic sources, it works through the more established use cases first—yield forecasting, precision irrigation, pest and disease detection—before turning to newer and less settled ground: autonomous machinery, machine-vision quality inspection, predictive maintenance, and traceability. Along the way it includes diagrams of the underlying technology stack and the flow of data across the value chain, plus a short summary of reported market and adoption figures. The final sections are more critical in tone, considering the technical, economic, and regulatory friction that still stands between where AI adoption is today and where it would need to be for the benefits to reach smaller producers as well as large ones.},
        keywords = {Agri-food systems; artificial intelligence; computer vision; food processing; food safety; machine learning; precision agriculture; supply chain.},
        month = {September},
        }