Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%.