Apple Leaf Diseases Detection and Classification using Deep Learning

Abstract

Timely diagnosis and identification of apple leaf diseases are imperative for preventing the spread of diseases and ensuring the sound development of the apple industry. Convolutional Neural Networks (CNNs) have achieved phenomenal success in the area of leaf disease detection, which can greatly benefit the agriculture industry. However, their large model size and intricate design continue to pose a challenge when it comes to deploying these models on lightweight devices. Although several successful models (e.g. EfficientNet and MobileNet) have been designed to adapt to resource-constrained devices, these models have not been able to achieve significant results in leaf disease detection task and leave a performance gap behind. This research gap has motivated us to develop an apple leaf disease detection model that can not only be deployed on lightweight devices but also can outperform the existing models. In this work, we propose AppVit, a hybrid vision model, combining the features of convolution and multihead self-attention, to compete with the best performing models. Specifically, we begin by introducing the convloution blocks that narrows-down the size of the feature maps and helps the model to encode local features progressively. Then, we stack VIT blocks in combination with convolution blocks allowing the network to capture non-local dependencies and spatial patterns. Embodied with these designs and a hierarchical structure, AppVIT demonstrates excellent performance on apple leaf disease detection task. Specifically, it achieves 96.38 % accuracy on plant pathology 2021 - FGVC8 with about 1.3 million parameters, which is 11.3% and 4.3% more accurate than ResNet-50 and EfficientNet-B3. The precision, recall and f-score of our proposed model on apple leaf disease detection and classification are 0.967, 0.959, 0.963 respectively.