VGG19-based Transfer learning for Rice Plant Illness Detection

Bacteria, viruses, and fungi impact plant development. So early diseases diagnosis and prevention are crucial for plants. Many different machine-learning approaches are used to identify and classify rice plant illnesses. As technology improves, Deep Learning methods, including DL algorithms, boost precision and accuracy. DL gives segmented-part detail. Early plant disease specialists saw and analyse without a sophisticated system. This takes additional labour and processing time. A physical analysis isn't practical owing to the size of the agricultural industry. However, image processing using Deep Learning may be utilized to identify and classify rice plant diseases. It detects and classifies plant illnesses automatically. The report also covers present successes, limits, and proposals for newborn investigation linked with rice insufficiency disorders. Pre-processing, picture segmentation, feature extraction, and illness classification are discussed. A technique for detecting rice leaf disease based on Deep Convolution Neural Network transfer learning. The method that has been suggested can differentiate between six categories, namely healthy, Rice Tangro, Rice Yellow Mottling, Bacterial Blight, Leaf Streak and Leave Scalding. The upgraded dataset that is not normalized has an accuracy of 97%. The precision, recall, and specificity scores and the F1-score were respectively 97%, 97%, 99% and 97%. The recommended new method performed better than earlier methods that used datasets of the same size or a size comparable to those used in the literature.