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. Inceptionresnetv2 is the combination of residual net and inception net, a hybrid of the inception and resnet model. The aim of the paper is to find whether the original models or the hybrid model is efficient in classifying cassava plant disease detection.
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