School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Pest Manag Sci. 2024 Dec;80(12):6082-6095. doi: 10.1002/ps.8336. Epub 2024 Aug 14.
Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control.
Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively.
Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.
条锈病（Puccinia striiformis f. sp. tritici）是小麦生产的毁灭性灾害，对中国东部主要小麦产区的产量和粮食安全构成严重威胁。有必要在春季关键小麦生长期间监测条锈病的进展，通过为病害预测模型提供及时的校准以及及时的绿色防控来支持其预测。
在三个小麦生长时期（拔节期、抽穗期和灌浆期）获取了三个用于病害的 Sentinel-2 图像。为每个生长时期的病害提取了光谱、纹理和颜色特征。然后获得了三个时期特定的特征集。考虑到三个时期田间病害流行情况的差异，建立了三个针对时期的监测模型，以绘制春季条锈病损害的进展情况并跟踪其时空变化。然后根据三个时期的田间病害实况数据（拔节期 87 个，抽穗期 183 个，灌浆期 155 个）对模型的性能进行了验证。验证结果表明，基于我们的监测模型组对小麦条锈病损害进展的描述是现实和可信的。拔节期健康和患病像素分类监测模型的整体准确率达到 87.4%，抽穗期和灌浆期病害指数回归监测模型的决定系数（R）分别为 0.77（抽穗期）和 0.76（灌浆期）。基于模型组结果的整个研究区域条锈病进展的时空变化检测表明，在拔节期到抽穗期和抽穗期到灌浆期符合预期病害时空发展模式的面积比例分别达到 98.2%和 84.4%。
我们的拔节期、抽穗期和灌浆期针对的监测模型组克服了大多数现有监测模型仅基于单阶段遥感信息的局限性。它在揭示春季小麦条锈病的时空流行方面表现良好，能够及时更新病害趋势以优化病害管理，并为及时修正模型的病害预测提供依据。© 2024 化学工业协会。
