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@article{182950,
        author = {Christina E A and Hemalatha N and Sathish Kumar Narayanan},
        title = {A Review of UAVs, Remote Sensing and AI for Precision Agriculture in Coffee Farming},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {8-15},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182950},
        abstract = {Coffee, one of the world’s most widely traded agricultural commodities, is increasingly vulnerable to challenges such as climate variability, disease outbreaks, and the limitations of conventional farming practices. In response, precision agriculture has emerged as a growing method, integrating technologies such as unmanned aerial vehicles (UAVs), satellite-based imaging, and artificial intelligence (AI) to improve crop monitoring, yield estimation, and disease management. This review synthesizes findings from 32 recent peer-reviewed studies encompassing innovations in remote sensing-based inventory mapping, machine learning-based yield forecasting, and deep learning models for real-time disease identification. It evaluates the effectiveness of spectral indices like NDVI and CRDI, object-oriented image analysis, and mobile-based convolutional neural networks (CoffNet, MobileNetV2) across coffee-growing regions including Brazil, India, and Vietnam. The review also explains existing challenges, including model generalizability and economic accessibility, and proposes future directions such as hyperspectral imaging, federated learning frameworks, and integrated mobile-UAV systems. The overarching aim is to provide a consolidated framework for advancing research, policy, and field deployment of intelligent systems in the global coffee sector.
Coffee, one of the world’s most widely traded agricultural commodities, is increasingly vulnerable to challenges such as climate variability, disease outbreaks, and the limitations of conventional farming practices. In response, precision agriculture has emerged as a growing method, integrating technologies such as unmanned aerial vehicles (UAVs), satellite-based imaging, and artificial intelligence (AI) to improve crop monitoring, yield estimation, and disease management. This review synthesizes findings from 32 recent peer-reviewed studies encompassing innovations in remote sensing-based inventory mapping, machine learning-based yield forecasting, and deep learning models for real-time disease identification. It evaluates the effectiveness of spectral indices like NDVI and CRDI, object-oriented image analysis, and mobile-based convolutional neural networks (CoffNet, MobileNetV2) across coffee-growing regions including Brazil, India, and Vietnam. The review also explains existing challenges, including model generalizability and economic accessibility, and proposes future directions such as hyperspectral imaging, federated learning frameworks, and integrated mobile-UAV systems. The overarching aim is to provide a consolidated framework for advancing research, policy, and field deployment of intelligent systems in the global coffee sector.},
        keywords = {Coffee agriculture, UAV, remote sensing, yield prediction, deep learning, disease detection, spectral indices, precision farming},
        month = {},
        }