Leaf Diseases Prediction Pest Detection and Pesticides Recommendation using Deep Learning Techniques

Problems with crop production and plants are common in India, affecting rural farmers, the agriculture industry, and the economy as a whole. Leaf plays a significant role in Plants and Crops because, depending on its condition, it provides information in advance about the quantity and quality of agricultural yield. This study has proposed a Deep Neural Network (DNN) algorithm based disease classification, pesticide recommendation and pre-processing of leaf images obtained from a plant village dataset. After expanding the plant leaf image’s data set through geometric manipulation, the extended dataset is separated into a train dataset and a test dataset. After that, the large ImageNet image data set is used to train the ResNet-50 models. In this study, the pre-learned data model constraints are conveyed to the lesser model image dataset of plant infections to train the leaf diseases, pest recognition, and pesticides recommendation model founded on the concept of transfer learning. On a number of diseased leaves, this method is tested. The results of experiments demonstrate that using the transfer learning-based CNN method will improve the higher accuracy and performance efficiency for plant sprig image detection. It is capable of rapidly and precisely diagnosing crop diseases, reducing pesticide and fertilizer use, and increasing crop yield and quality.