Zanthoxylum rust (ZR) poses a significant threat to Zanthoxylum bungeanum Maxim.(ZBM) production, impacting both the yield and quality. The lack of current research on ZR using unmanned aerial vehicle (UAV) remote sensing poses a challenge to achieving precise management of individual ZBM plant. This study acquired six UAV hyperspectral images to create a ZR inversion dataset . This dataset, to our knowledge, is the first dataset for remote sensing deep learning (DL) of ZR using UAV. To facilitate automated extraction of individual ZBM plant and the quantitative inversion of ZR disease index (DI), we introduced the object-based quantitative inversion framework (OQIF). OQIF achieved high accuracy in recognizing ZBM (average precision at an intersection over union threshold of 0.5 was 90.0 %). Remarkably, OQIF demonstrates outstanding quantitative inversion results for ZR DI (R2 = 0.90, RMSE = 3.97, n = 8166). For DI < 10, the RMSE was 2.48, showcasing early detection capability. Our research has significant implications for ZBM cultivation and precision management, pioneering object-based quantitative inversion for tree diseases and yield estimation, with potential for early ZR detection.

中文翻译：

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利用无人机高光谱影像和深度学习对花椒锈病指数进行基于对象的定量反演


花椒锈病 （ZR） 对花椒锈病构成重大威胁。（ZBM） 生产，从而影响产量和质量。目前缺乏使用无人机 （UAV） 遥感的 ZR 研究，这为实现单个 ZBM 工厂的精确管理带来了挑战。本研究获取了 6 张无人机高光谱图像以创建 ZR 反演数据集。据我们所知，该数据集是使用无人机对 ZR 进行遥感深度学习 （DL） 的第一个数据集。为了促进单个 ZBM 植物的自动提取和 ZR 病害指数 （DI） 的定量反演，我们引入了基于对象的定量反演框架 （OQIF）。OQIF 在识别 ZBM 方面取得了很高的准确率（在并集阈值为 0.5 的交点处的平均精度为 90.0 %）。值得注意的是，OQIF 对 ZR DI 表现出出色的定量反演结果 （R2 = 0.90，RMSE = 3.97，n = 8166）。DI < 10 的 RMSE 为 2.48，展示了早期检测能力。我们的研究对 ZBM 种植和精确管理具有重要意义，开创了基于对象的树木病害定量反演和产量估计，具有早期 ZR 检测的潜力。