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.

翻译：过去十年间，基于视觉数据的植物病害识别已涌现出多种图像处理方法与算法。近年来，深度神经网络（DNN）在该任务中日益普及。然而，受计算能力限制及植物病害特征多样性影响，传统图像处理方法与基于DNN的方法在实时检测中均面临显著的性能瓶颈。本文提出一种基于图像四叉树分解与特征同步学习的新型植物病害识别与定位技术。该算法在保持较低计算负荷的同时，显著提升了高分辨率图像的识别精度与收敛速度，因而非常适合部署于远程图像采集与病害检测系统的独立处理器中，尤其适用于在大型农田作业的无人机与机器人平台。本文提出的混合技术通过融合传统图像处理方法与多尺度DNN模型的优势，实现了更快速的推理。针对马铃薯与番茄作物的四类病害，该技术的F1分数达到约0.80。