Revolutionizing Potato Crop Health Using Advanced Image Analysis with Efficient Classification Algorithm

accurate detection of diseases is crucial for enhancing and increasing crop production in precision agriculture. The work is centered on developing a method that allows a CNN to learn to determine features in crop disease image snippets that can be said to be significant. Moreover, an efficient classification algorithm is also used in the proposed work to classify specific diseases more quickly and with greater confidence. The training process included applying the back propagation technique to update the weights of the generator network in order to increase the quality of generated images in a way that minimizes the probability of producing fake identities. The images that are related to the crop disease have been collected from the Village Potato Plant dataset for this purpose. The experiments were also done using the CNN which was trained at 15, 30 & 45 epochs for the Adam optimizer. Furthermore, it was evident that an increase in the number of training epochs in general resulted in the increase in confidence score. Combining both the CNN and the classification algorithm helps the system find an optimal balance between model confidence and processing speed that makes it perfect to be used in real-time crop disease classification.