Deep learning approach for Cassava Phytoplasma Disease image classification
Crop disease identification through image analysis is proven to be more cost effective and less time consuming than traditional approaches. In this study, deep learning is employed to predict the occurrence of Cassava Phytoplasma Disease (CPD). A pre-trained Convolutional Neural Network (CNN) architecture, Inception-ResNet-V2, was utilized on a cassava image dataset through transfer learning. The model has 83.78% accuracy, 80.95% precision, 89.47% recall, and 0.836 area under the curve (AUC). This rapid disease identification would be beneficial to farmers as they can isolate CPD-infected crops at a much earlier time.