Optimized Feature Extraction Model on RCNN and BPNN Models for Grape Disease Detection
Keywords:
Deep Learning, Grape Leaf , Disease, Prediction , R-CNN, BPNNAbstract
One of the most pressing problems facing farmers today is the proliferation of plant diseases, which pose a serious threat to the safety of the food we consume. Therefore, it is crucial to detect these diseases early and find viable treatments to prevent them. This study analyses numerous methods for diagnosing and classifying diseases that might affect grapevines. The aim of this research is to provide a thorough overview of the many techniques used to identify and categorise grape leaf diseases. Important image processing procedures for disease prediction are discussed, including picture collection, data pre-processing, image segmentation, feature extraction, and image classification. Convolution Neural Network (R-CNN and BPNN are only some of the standard image processing and detection and classification methods covered. To better assist researchers in determining which methods can be selected to enhance grape leaf disease identification and classification efficiency, we have identified the differences caused by deep learing techniques and the various processes used to obtain various results by referencing a number of articles.
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