A Deep Learning Approach to Early Plant Disease Detection and Diagnosis Using Advanced Imaging

Abstract

Early and accurate plant disease detection is crucial for sustainable agriculture. This research proposes an Ensemble Residual Sequence Net (ERSN) architecture, combining LSTM and ResNet-50, for classifying plant diseases in pearl millet. Targeting diseases like downy mildew, ergot, blast, rust, and smut, the model achieved high accuracy (94.7%), precision, recall, F I-score, specificity, and sensitivity. This innovative approach demonstrates the potential of deep learning in enhancing plant disease management and safeguarding crop yields.

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