Paper Contents
Abstract
Recognizing disease on the plant is very crucial to avoid any damages to the yield and other agricultural products. The symptoms can be seen on parts of the plant such as leaves, stems, lesions, and fruits. Changes in color along with showing spots on the leaf also demonstrate some symptoms. This form of identification needs manual observation along with pathogen detection, which in the long run can be expensive and time consuming. The objective of the project is to locate and accurately classify the disease from the leaf images. The processes involve in the procedure are Preprocessing, Training and Identification. In case of disease identification, features of the leaf like major axis, minor axis and so on, are retrieved and passed into a classifier which classifies the extracted data. We use cassava leaves in our project to study its disease. For the accuracy in the project, SqueezeNet and ResNet-50 were used as the existing and proposed systems respectively. The results have proven that ResNet-50 works better than SqueezeNet. Diagnosis of the plant was done using MATLAB, an effective tool for detecting plant diseases based on images.
Copyright
Copyright © 2025 Laasya,Akshitha. This is an open access article distributed under the Creative Commons Attribution License.
