Paper Contents
Abstract
Identification of plant disease is the key to prevent the losses in the yield and quantity of the agricultural product. The studies of the plant disease means the study of visually observable patterns seen on the plants. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor plant disease manually. It requires tremendous amounts of work expertise in the plant disease and also require the excessive processing time. Hence image processing is used for detection of plant disease by capturing the image of the leaves and comparing it with the dataset. Dataset consist of different plants in the image format. Apart from the detection users are directed where different pesticides are displayed. In this project, we investigate the problem of visual plant disease recognition for plant disease diagnosis. Compare with other types of images, plant disease images generally exhibit randomly distributed lesion, diverse symptoms and complex backgrounds, and these are hard to capture discriminative information. Facilitate the plant disease recognition research, we use a plantvillage plant disease dataset with plant disease categories and images. Based on this dataset, we tackle plant disease recognition via visual regions and loss to emphasize diseased parts. Diseases are investigated utilizing different image processing techniques and diagnosed so that farmers can overcome from yield and financial loss.
Copyright
Copyright © 2023 Tanuja Shivaji Gaygoye . This is an open access article distributed under the Creative Commons Attribution License.
