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@article{180725,
        author = {Atharva Tanpure and Akash yeola and Hrishikesh Chaudhari and Sanskrut Wadettiwar and Nikita Kolambe},
        title = {A Hybrid Deep Learning Architecture with Attention Mechanism for Plant Nutrient Deficiency Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2964-2969},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180725},
        abstract = {Early identification of nutrient deficiencies in plants is essential for boosting crop productivity, preserving plant health, and promoting sustainable farming. In this study, we introduce a hybrid deep learning model that utilizes ensemble learning methods to automate the detection of nutrient defi- ciencies through analysis of leaf images. Our system integrates the lightweight efficiency of MobileNet with the deeper feature extraction capabilities of ResNet50. These features are then passed through ensemble classifiers such as Random Forest and Gradient Boosting, enhancing the overall classification accuracy. The process includes image preprocessing, feature extraction, and the deployment of a reliable classification mechanism suitable for real-time use. Experimental evaluations show that the ensemble approach consistently outperforms individual models, providing higher accuracy and robustness across different plant types. This work contributes to the field of precision agriculture by offering a scalable and effective solution for the early detection of plant nutrient issues, ultimately supporting more informed and timely crop management decisions.},
        keywords = {Plant Nutrient Deficiency, Ensemble Learning, Image Processing, Precision Agriculture},
        month = {June},
        }