小麦条锈病精准监测：高光谱与日光诱导叶绿素荧光技术解密

Precision Monitoring of Wheat Stripe Rust: Unraveling Hyperspectral and Solar-Induced Chlorophyll Fluorescence Technologies

小麦条锈病严重威胁粮食安全，实现早期准确监测既需要高灵敏技术支持，也需切实可行的硬件设备。高光谱成像和日光诱导叶绿素荧光（SIF）技术因其敏感捕捉植株生理和光谱变化的能力，正成为小麦病害监测的有力工具。接下来，我们结合三项具体研究案例，展示光谱技术的应用。

Wheat stripe rust, a serious threat to food security, requires highly sensitive technological support and practical hardware for early and accurate monitoring. Hyperspectral imaging and Sun/Solar-Induced Chlorophyll Fluorescence (SIF) technologies, known for their ability to sensitively capture physiological and spectral changes in plants, are becoming powerful tools for monitoring wheat diseases.

Here, we present three specific research case studies that demonstrate the application of these spectral technologies.

基于日光诱导叶绿素荧光（SIF）的冠层与叶片尺度监测

某团队以冬小麦自然感染条锈病为研究对象，在陕西田间采集冠层及叶片级别数据，使用日光诱导叶绿素荧光测量系统结合高光谱设备采集SIF信号、荧光产量（ΦF）、归一化植被指数（NDVI）等数据。

研究发现，冠层尺度上多个荧光相关指标与病情严重度均显著相关，其中ΦF-r（SIF/NIRvR，NIRvR是植被的近红外辐射度）在病害早期对植株生理压力的敏感性优于传统光谱指标；而传统光谱指标如NDVI在病害后期的监测表现仍具优势。

这意味着SIF信号与传统光谱指数具有互补优势，二者结合可实现更全面、更精准的小麦条锈病监测。

Canopy and Leaf Scale Monitoring Based on Solar-Induced Chlorophyll Fluorescence (SIF)

A research team focused on naturally infected winter wheat with stripe rust in Shaanxi Province, collecting canopy and leaf-level data in the field. They employed a SIF measurement system integrated with hyperspectral equipment to capture SIF signals, fluorescence yield (ΦF), and normalized difference vegetation index (NDVI).

The study found that several fluorescence-related indicators at the canopy scale were significantly correlated with disease severity levels. Notably, ΦF-r (SIF/NIRvR, where NIRvR refers to near-infrared radiation of vegetation) exhibited superior sensitivity to physiological stress in plants during the early stages of the disease compared to traditional spectral indices. In contrast, traditional spectral indices like NDVI remained effective in monitoring during the later stages of the disease.

This indicates that SIF signals and traditional spectral indices possess complementary advantages. When combined, they can achieve a more comprehensive and precise monitoring of wheat stripe rust.

a.研究区，b.冠层光谱测量实验装置，c.研究区小麦的三种形态

Study area (a), experimental set-up of canopy spectral measurements (b), and three morphological of wheat in study area (c).

轻病条件下不同信号与 SL 的关系 （SL<20%）。（a–f） 是冠层尺度数据;（g，h） 是叶尺度数据。红带内的红线表示回归线和95%置信区间。

Relationship between different signals and SL under comprehensive experimental conditions.  (a–f) are canopy-scale data; (g,h) are leaf-scale data. The red lines with band denote the regression line and 95% confidence interval.

利用小波能量系数的协同冠层SIF监测冬小麦条锈病

另一研究团队结合小波能量系数方法，协同使用冠层SIF信号，在河北廊坊对冬小麦条锈病进行定量监测。采用高光谱成像仪采集冠层光谱及叶绿素荧光数据，深入分析了光谱与荧光信号对病害动态变化的响应。

研究中建立了多因子融合模型，揭示了病害影响下作物光合生理的群体特征表现，显著提升了病害检测的准确性和时效性。该方法为利用SIF进行小麦条锈病动态监控提供了理论和技术支持。

Monitoring Winter Wheat Stripe Rust Using Collaborative Canopy SIF with Wavelet Energy Coefficients

Another research team employed a wavelet energy coefficient method, utilizing canopy SIF signals to quantitatively monitor winter wheat stripe rust in Langfang, Hebei Province. They collected canopy spectral and chlorophyll fluorescence data using hyperspectral imaging equipment for in-depth analysis of the response of spectral and fluorescence signals to dynamic changes in disease.

They established a multi-factor integration model that revealed the impacts of stripe rust on the photosynthetic physiology of the crops, significantly improving the accuracy and timeliness of disease detection. This method provides theoretical and technical support for employing SIF in the dynamic monitoring of wheat stripe rust.

冠层光谱。a.不同疾病严重程度下的原始光谱；b.DI 与反射率之间的相关系数曲线

Analysis based on canopy spectra. (a) the original spectra under different disease severity; (b) the curve of correlation coefficient between DI and reflectance.

技术框架 / Methodological framework of the monitoring model for stripe rust

无人机高光谱成像技术融合叶绿素荧光指标实现条锈病早期检测

该团队还进行了另外一组研究：利用无人机搭载高光谱成像仪，结合多种色素及相关光谱指数，检测小麦条锈病。该团队通过航拍获取大范围田间高光谱数据，提取病斑色素特征和光谱指标，融合叶绿素荧光相关参数进行建模分析。

结果表明，该方法可实现条锈病的高精度早期检测，适用于大范围快速监测与病害扩散风险评估，为农业精准防控提供可靠技术支撑。

Early Detection of Stripe Rust Using UAV-Mounted Hyperspectral Imaging Technology and Chlorophyll Fluorescence Indicators

In a different research initiative, the team utilized UAVs equipped with hyperspectral imaging systems to detect wheat stripe rust, combining various pigments and related spectral indices. They obtained extensive hyperspectral data through aerial surveys, extracting pigment characteristics and spectral indices from the diseased patches, which were then modeled together with the chlorophyll fluorescence parameters.

The results demonstrated that this method could achieve high-precision early detection of stripe rust, suitable for large-scale rapid monitoring and disease spread risk assessment, thereby providing reliable technical support for precision agricultural management.

实验区位置和样地分布。A表示实验区域的位置；B表示无人机高光谱数据采集活动；C表示无人机高光谱图像和样本位置；D表示不同侵染期健康和患病样本的状态。D1-D3代表健康样本，D4-D6分别代表接种后7天、16天和23天（DPI）的患病样本。

Experimental area location and plot distribution. A represents the location of the experimental area; B represents UAV hyperspectral data acquisition activity; C represents UAV hyperspectral image and sample location; D represents the status of healthy and diseased samples at different infestation periods. D1-D3 represent healthy samples, and D4-D6 represent diseased samples at 7, 16, and 23 days post-inoculation (DPI), respectively.

Exponent的产品优势与解决方案

为支持广泛应用，我司自主研发日光诱导叶绿素荧光(SIF)监测系统，具备强大的实时采集能力；同时代理高性能的国产高光谱成像仪，满足从地面、塔基到无人机平台的多场景需求。

用户可利用这些硬件设备，自主开发分析模型，实现小麦条锈病的早期预警、动态监控与精准防控，真正实现农业生产的数字化和智能化转型。

此外，我们的设备支持集成到农业机械中，辅助农机实现精准、智能的高效喷药作业，有效提升除病效率，降低农药使用量，推动绿色农业发展。

欢迎联系了解设备详情及定制化技术服务，让光谱技术助力智慧农业，守护粮食安全！

Exponent's Product Advantages and Solutions

To support widespread application, our company has independently developed a Solar-Induced Chlorophyll Fluorescence (SIF) monitoring system with powerful real-time acquisition capabilities. We also represent high-performance domestic hyperspectral imaging systems, catering to various scenarios from ground, tower, to UAV platforms.

Users can utilize these hardware devices to develop their analytical models, enabling early warnings, dynamic monitoring, and precise prevention of wheat stripe rust, effectively realizing the digital and intelligent transformation of agricultural production.

Additionally, our devices can be integrated into agricultural machinery, assisting in precise and intelligent pesticide application, thus improving disease control efficiency and reducing pesticide usage, promoting the development of sustainable agriculture.

We welcome inquiries for more details about our equipment and customized technical services, empowering smart agriculture through spectral technology and safeguarding food security!

案例来源 / Source

1. Du, K., et al. "An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence." Remote Sensing, vol. 15, no. 3, 2023, p. 693.

2. Ren, Kehui, et al. "Monitoring of Winter Wheat Stripe Rust by Collaborating Canopy SIF with Wavelet Energy Coefficients." Computers and Electronics in Agriculture, vol. 215, 2023, p. 108366.

3. Guo, Anting, et al. "Improved Early Detection of Wheat Stripe Rust through Integration Pigments and Pigment-Related Spectral Indices Quantified from UAV Hyperspectral Imagery." International Journal of Applied Earth Observation and Geoinformation, vol. 135, 2024.