Detection and Classification of Cassava Diseases using Machine Learning
S. V. Krishna Jagadish
Abstract
Abstract: Cassava (Manihot esculenta) is a crucial food crop sustaining millions of people worldwide. However, the presence of diseases in cassava plants poses a significant threat to agricultural productivity. Traditional methods of disease detection are often labor-intensive, subjective, and prone to human error. To address these challenges, this research focuses on developing an automated system for cassava plant disease detection using machine learning. After the algorithms have been trained on the dataset, the accuracy of the algorithms is compared, the photos are categorised, and preventions for unhealthy plants are proposed. Apart from detection, this aims to support and help the greenhouse farmers in an efficient way. Plant disease identification by visual way is more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take less efforts, less time and become more accurate.