Plant Disease Detection

Currently, disease detection is important for better crop yield and quality. Deterioration in the quality of agricultural products, plant diseases can lead to huge economic losses for individual farmers. Plants are the food source of the earth. Plant diseases therefore pose a great threat that can cause many infections, while the usual diagnosis is mainly made by examining the plant body for the presence of visual symptoms. In this research, we proposed a deep convolutional neural network to detect plant diseases from leaf images. To detect a disease, we have a dataset that consists of different open datasets and contains images of different plants. All of the steps required to implement this disease detection are fully described in the paper, starting with collecting images to create a database for the data. With the help of this research work, we can find out the disease and reduce the economic losses. Methods based on machine learning can be used to identify diseases since they are mainly applied to data superiority results for specific tasks. In this approach, a comprehensive review of the different techniques used in plant disease detection using artificial intelligence (AI) based machine learning and deep learning techniques was conducted.