Generation and Utilization of an Augmented Cashew Leaf Dataset for Disease Analysis using Transfer Learning

The scarcity of publicly available datasets for the detection of cashew leaf disease (DCLD) (Anacardium occidentale L.). This study represents the development and evaluation of cashew leaf disease dataset of three districts. Here, Dataset_1 with 1274 original images and Dataset_2 with 2548 augmented images to increase its diversity. Images were capture using smartphone with natural field conditions, background, various resolutions, angles, Pre-processing and data augmentation techniques were applied to enhance model performance.

Multiple deep learning models, including DenseNet121, EfficientNetB0, restNet50 and Vision Transformer were trained using transfer learning to classify four cashew leaf condition: Anthracnose, Healthy, Leaves Spot and Powdery Mildew.

DenseNet121 achieved high class wise precision and recall, with overall accuracy between 96% to 97%. EfficientNetB0 improved upon this result, attaining overall accuracy between 96% and 98% with F-1 score up to 97%. RestNet50 and ViTB16 achieved the highest performance, with over accuracy to 99.93% respectively.