Identification of paddy blast disease field images using multi-layer CNN models

Farmers and agricultural experts can take action on many areas of paddy crop handling and management practices with the use of actionable information from the in-field diagnosis of paddy blast disease. To successfully diagnose the blast disease affecting fifteen different paddy crop varieties, three transfer learning multi-layer convolutional neural network (CNN) models, such as CapsNet, EfficientNet-B7, and ResNet-50, are presented in this paper. The field images of blast disease are captured and classified based on disease severity levels, such as low, medium, high, and severe. The study employing the CapsNet model with a dataset consisting of a total of 20,000 labeled images demonstrated significant results with a testing efficiency of 90.79% and a validation efficiency of 93.29%. The ResNet-50 and EfficientNet-B7 models have yielded average testing efficiencies of 85.10% and 88.72%, respectively. On the held out blast disease affected paddy field image dataset, the CapsNet model outperformed the EfficientNet-B7 and ResNet-50 CNN models in terms of both classification efficiency and computational efficiency.