A Lightweight Deep Learning Framework for Real-Time Plant Leaf Disease Classification
Abstract
Agricultural productivity is also at risk of plant diseases, especially in areas where the lack of experts makes it hard to diagnose the disease timely. Conventional manual inspection is slow, subjective and impractical when dealing with large scale monitoring. To mitigate this issue, this paper introduces a lightweight deep learning architecture that will be used to classify plant leaf diseases in real-time. The given solution makes use of the idea of transfer learning based on MobileNetV3 to ensure a high level of classification accuracy and a low complexity of computations. The Plant Village data, which is publicly available, and consists of various leaf images of many different crops and disease classes, was used to carry out the experiments. Normal preprocessing and data augmentation methods were used to enhance the generalization of the model. The proposed model had an overall accuracy of 94% with a high level of precision and recall by all the classes. Moreover, the lightweight architecture can be quickly inferred and deployed to a mobile and edge device, which makes it very appropriate. The findings show that an effective deep learning model can deliver valid and real-time diagnosis of a disease, which can be used to support practical agriculture and sustainable crop management.