Research
Detection of Casava Plant related Diseases using Deep Learning
Author(s): Megha Mankal*, Chinnapani Kiran Kumar and Dr. Samala Nagaraj
Cassava plant is known for the high carbohydrate source. But it is vulnerable to various diseases, which sabotage food security in sub-Saharan Africa. Cassava plant related disease identification should be automated to handle the crisis. Disease detection through image classification and recognition is known to be the best and cost-effective method for early detection and prevention of diseases to prevent further damage of a plant. The dataset contains 21,397 labelled images collected from Uganda. The study trains the dataset using three deep convolutional neural networks to identify the diseases and a healthy plant. The four types of diseases are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB). The present study uses Inceptionresnetv2, Inceptionv3, and Resnet50 models and comparing their accuracies. Inc.. View More»
DOI:
10.14303/irjps.2021.16