Detection and Classification of Cassava Diseases Using Machine Learning

Abstract

In this work, we develop and trained machine learning models for the detection and classification of cassava (Manihot esculenta Crantz) disease as Blight or Mosaic. 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 two categories from over 18,000 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 Cubic Support Vector Machine (CSVM) model developed for health diagnosis and the Coarse Gaussian Support Vector Machine (CGSVM) model developed for disease detection which yielded accuracies of 83.9% and 61.6% respectively.