结合图像分解与深度学习，实现农作物病害实时精准识别定位
A Hybrid Technique for Plant Disease Identification and Localisation in Real-time
- 用四叉树分解图像，分层处理特征以降低计算负担
- 在马铃薯和番茄四种病害上达到约0.80的F1分数
- 适合部署在无人机、机器人等边缘设备上实时运行
过去十年中，已有多种基于视觉数据的植物病害识别图像处理方法和算法被提出。近年来，深度神经网络（DNN）在此任务中逐渐流行。然而，传统图像处理与基于DNN的方法在高分辨率图像的实时检测中均因计算限制和病害特征多样性面临显著性能瓶颈。本文提出一种新型混合技术，通过图像的四叉树分解与特征学习并行进行，显著提升高分辨率图像下的识别准确率和收敛速度，同时保持较低计算负载。该方法在马铃薯和番茄作物的四种病害分类中取得了约0.80的F1分数，适用于部署于独立处理器的远程图像采集与病害检测系统，尤其适合安装在无人机或机器人上用于大面积农田作业。该技术融合了传统图像处理与DNN模型在不同尺度上的优势，实现更快推理速度。
原文摘要 · Abstract (English)
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.
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