Detection of cassava plant disease using convolutional neural network

The detection of the plant disease affecting cassava shields this crop from harm. The cassava plant, the foremost producer of carbohydrates in the world, has emerged as a crucial energy source for those living in tropical areas. Yet, it was especially susceptible to bacterial, fungal, and viral infections, which hindered plant development and consequently the action. The major objective of this study is to assist farmers in swiftly identifying four different types of cassava plant disease before they cause serious harm to the plant community. The deep learning approach of the convolutional neural network, in particular, is widely applied in image recognition. models for identifying cassava plant diseases. Segmentation and filtering techniques are used in Convolutional neural networks for the proposed work. The suggested model has undergone testing and training using images of cassava plant disease. The results of the experiments demonstrate that the suggested model performs better and has a 99% accuracy rate.