CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves

Plant diseases play a significant role in reducing crop productivity. Nobody will be able to consume the crops because they have been contaminated with different illnesses. As a result, farmers may face huge losses. Cassava is a staple crop in some nations, so this issue may have severe impact on the economy. Detecting plant diseases manually takes time and is prone to errors. It is not always a reliable method for discovering and avoiding the propagation of plant diseases. In order to overcome these issues, cutting-edge technology like machine learning and deep learning may help with early plant disease identification. The main goal of the work is to use deep learning to image classification in order to accurately identify diseases that especially impact cassava plants. This identification may make it possible to implement early intervention measures like specific application of chemicals or confinement of contaminated crops. Each and every test and training image comes from a rural area in the natural world. Using a specific collection of data, the model has been verified to ascertain its true results. In a nutshell this work uses deep learning methods to combat cassava diseases, supporting agricultural practices and food security. The development of a precise disease detection and prevention model has the potential to significantly increase the capacity for recovery of the cassava crop, improving food production and the quality of life for those whose livelihoods depend on this paramount crop.