Automated Plant Disease Detection and Classification in Leaf Image

The identification of disease on the plant is a very important key to prevent a heavy loss of yield and the quantity of agricultural product. The symptoms can be observed on the parts of the plants such as leaf, stems, lesions and fruits. The leaf shows the symptoms by changing color, showing the spots on it. This identification of the disease is done by manual observation and pathogen detection which can consume more time and may prove costly. The aim of the project is to identify and classify the disease accurately from the leaf images. The steps required in the process are Preprocessing, Training and Identification. For identification of disease features of leaf such as major axis, minor axis etc. are extracted from leaf and given to classifier for classification. In our project we have used cassava leaf for identifying its disease. We have used algorithms namely SqueezeNet as existing and ResNet-50 as proposed system in terms of accuracy. From the results its proved that ResNet-50 works better than SqueezeNet. The tool we have used for the detection of leaf disease is MATLAB.