Cassava Leaf Disease Classification using Separable Convolutions UNet

Abstract

In this work, we develop and trained deep learning models for the segmenting and classification of cassava leaf disease as Blight or Mosaic. As the second-largest provider of carbohydrates in Africa, cassava is a key food security crop grown by smallholder farmers because it can withstand harsh conditions. At least 80% of household farms in Sub-Saharan Africa grow this starchy root, but viral diseases are major sources of poor yields.  Our emphasis here was on two major cassava diseases that occur in Nigeria which are the Cassava Mosaic Disease (CMD) and the Cassava Bacterial Blight disease (CBBD). A total of 46 models were trained in five categories from over 21397 cassava leaf images was collected at different times of day containing leaves at different levels of symptom manifestation. One model diagnosed the healthy leaf and the other model detected the diseases that are present on the leaf when diagnosed as an unhealthy leaf and two most accurate models were exported. A 5-fold cross-validation was used to test the Separable Convolutions UNet model developed for health diagnosis and the Separable Convolutions UNet model developed for disease detection which yielded accuracies of 83.9% and 61.6% respectively.