Identification of Diseases in Cassava Leaves using Convolutional Neural Network

Abstract

Automating the detection of different types of diseases in plants is one of the most complex recent challenges faced by agricultural experts all over the world. Cassava is loaded with carbohydrates and is mostly cultivated by small farmers in Sub-Saharan Africa as a security crop as it is capable of growing amidst drought which affects mostly all the other crops directly and adversely. At present, farmers are completely dependent on plant health experts to come and have a weekly or bi-weekly random inspection of the plants. They collect leaf samples from 3 different parts of the plant – top, middle, and lower parts and send them to the lab for testing. This takes up a lot of time and does not leave enough time for farmers to find a solution once the plants get affected. Therefore, it is important for farmers to detect the diseases as early as possible. To tackle this problem, we propose a disease detection model for cassava plants using a Convolutional Neural Network (CNN). The features in the cassava plant images which signify the presence of disease will be automatically learned by the Neural Network. The farmers cannot detect these features manually. This research was conducted using a dataset of 21,397 labeled images collected during a regular survey in Uganda comprising four diseases namely Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), Cassava Mosaic Disease (CMD) and the dataset also contains healthy images of the plant. Most of the images in this dataset were taken by farmers in their gardens to provide a realistic representation of how farmers would diagnose these diseases in real life. These images were annotated by experts at the National Crops Resources Research Institute (NaCRRI) in collaboration with the AI lab at Makerere University, Kampala.

Topics & Concepts