A Hybrid Technique for Plant Disease Identification and Localisation in Real-time
This addresses the problem of computational limitations in real-time disease detection for agricultural applications, though it is incremental as it builds on existing image processing and DNN methods.
The paper tackles real-time plant disease identification and localization by proposing a hybrid technique combining Quad-Tree decomposition and feature learning, achieving an F1 score of approximately 0.80 for four disease classes in potato and tomato crops.
Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.