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

小麥條銹病嚴重威脅糧食安全，實現早期準確監測既需要高靈敏技術支持，也需切實可行的硬件設備。高光譜成像和日光誘導葉綠素熒光（SIF）技術因其敏感捕捉植株生理和光譜變化的能力，正成為小麥病害監測的有力工具。接下來，我們結合三項具體研究案例，展示光譜技術的應用。

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.

基于日光誘導葉綠素熒光（SIF）的冠層與葉片尺度監測

某團隊以冬小麥自然感染條銹病為研究對象，在陜西田間采集冠層及葉片級別數據，使用日光誘導葉綠素熒光測量系統結合高光譜設備采集SIF信號、熒光產量（ΦF）、歸一化植被指數（NDVI）等數據。

研究發現，冠層尺度上多個熒光相關指標與病情嚴重度均顯著相關，其中ΦF-r（SIF/NIRvR，NIRvR是植被的近紅外輻射度）在病害早期對植株生理壓力的敏感性優于傳統光譜指標；而傳統光譜指標如NDVI在病害后期的監測表現仍具優勢。

這意味著SIF信號與傳統光譜指數具有互補優勢，二者結合可實現更全面、更精準的小麥條銹病監測。

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.研究區，b.冠層光譜測量實驗裝置，c.研究區小麥的三種形態

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

輕病條件下不同信號與 SL 的關系 （SL<20%）。（a–f） 是冠層尺度數據;（g，h） 是葉尺度數據。紅帶內的紅線表示回歸線和95%置信區間。

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.

利用小波能量系數的協同冠層SIF監測冬小麥條銹病

另一研究團隊結合小波能量系數方法，協同使用冠層SIF信號，在河北廊坊對冬小麥條銹病進行定量監測。采用高光譜成像儀采集冠層光譜及葉綠素熒光數據，深入分析了光譜與熒光信號對病害動態變化的響應。

研究中建立了多因子融合模型，揭示了病害影響下作物光合生理的群體特征表現，顯著提升了病害檢測的準確性和時效性。該方法為利用SIF進行小麥條銹病動態監控提供了理論和技術支持。

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.

冠層光譜。a.不同疾病嚴重程度下的原始光譜；b.DI 與反射率之間的相關系數曲線

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

技術框架 / Methodological framework of the monitoring model for stripe rust

無人機高光譜成像技術融合葉綠素熒光指標實現條銹病早期檢測

該團隊還進行了另外一組研究：利用無人機搭載高光譜成像儀，結合多種色素及相關光譜指數，檢測小麥條銹病。該團隊通過航拍獲取大范圍田間高光譜數據，提取病斑色素特征和光譜指標，融合葉綠素熒光相關參數進行建模分析。

結果表明，該方法可實現條銹病的高精度早期檢測，適用于大范圍快速監測與病害擴散風險評估，為農業精準防控提供可靠技術支撐。

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.

實驗區位置和樣地分布。A表示實驗區域的位置；B表示無人機高光譜數據采集活動；C表示無人機高光譜圖像和樣本位置；D表示不同侵染期健康和患病樣本的狀態。D1-D3代表健康樣本，D4-D6分別代表接種后7天、16天和23天（DPI）的患病樣本。

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的產品優勢與解決方案

為支持廣泛應用，我司自主研發日光誘導葉綠素熒光(SIF)監測系統，具備強大的實時采集能力；同時代理高性能的國產高光譜成像儀，滿足從地面、塔基到無人機平臺的多場景需求。

用戶可利用這些硬件設備，自主開發分析模型，實現小麥條銹病的早期預警、動態監控與精準防控，真正實現農業生產的數字化和智能化轉型。

此外，我們的設備支持集成到農業機械中，輔助農機實現精準、智能的高效噴藥作業，有效提升除病效率，降低農藥使用量，推動綠色農業發展。

歡迎聯系了解設備詳情及定制化技術服務，讓光譜技術助力智慧農業，守護糧食安全！

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!

案例來源 / 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.