Exploring Entropy pruning coupled with Capsule Neural Network(CapsNet) for leaf disease classification

Abstract

In an attempt to detect plant leaf diseases faster, accurately, and more efficiently, researchers have adopted deep learning methods using models like CNN and CapsNet with some success. However, there is a need for improvement as the current models are computationally expensive in terms of time and model complexity. Our work improves the Capsule Neural Network(CapsNet) model by adding convolutional layers to collect enough data and prune using the Entropy-based method. Entropy-based pruning reduces the number of characters in the model by getting rid of useless features and retaining only the useful features for detection. This considerably reduces the parameter size and reduces model complexities in terms of time and computation. We used F1, F5, and varying folds to assess accuracy in pruned mode against normal models. We tested our idea using 9080 images of tomatoes from PlantVillage and on three models, namely; ResNet-50, VGG-16, and CapsNet. CapsNet was the best among the pruned models with 98.9% followed by ResNet-50 with 93% and VGG-16 at 89.99%. We observed that pruning might be a Superior and less computationally expensive method than VGG-16 and ResNet-50. This implies that the accuracy of such models can be improved through the introduction of pruning.