Using Artificial Intelligence for Plant Disease Classification Based on Convolutional Neural Network

Abstract

The plant disease prediction is useful in increasing agricultural production. The plant disease diagnosis through deep learning is a branch of artificial intelligence (AI). It is very important to diagnose plant diseases to improve productivity in agriculture. The plant leaf disease analysis provides us a problem of plant leaf lowest accuracy. The plant for identifying plant disease is to prevent the yield loss of agricultural products. We have proposed a model to validate the data set of the various plants through image processing. The AI-based plant disease detection is very important for sustainable agricultural development. The diseased plant needs to be monitored manually. It requires a lot of work and too much expertise is needed to cope with the plant’s disease in time. The proposed system Convolution Neural Network image processing is to predict the tomato plant diseases. Preprocessing is done in plant leaf image dataset image using Gaussian filter, and then data cleaning, data reduction, and classification are done in disease detection. This describes a method for predicting the disease of plants using the image of their leaves. Further, the algorithm used for the detection of plant diseases and final result of classification in plant disease prediction is described. This demonstrates the technical feasibility of CNN for plant disease classification and provides the accurate result for AI solutions.