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

小麥條銹病嚴(yán)重威脅糧食安全，實現(xiàn)早期準(zhǔn)確監(jiān)測既需要高靈敏技術(shù)支持，也需切實可行的硬件設(shè)備。高光譜成像和日光誘導(dǎo)葉綠素?zé)晒猓⊿IF）技術(shù)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(jiān)測的有力工具。接下來，我們結(jié)合三項具體研究案例，展示光譜技術(shù)的應(yīng)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(dǎo)葉綠素?zé)晒猓⊿IF）的冠層與葉片尺度監(jiān)測

某團(tuán)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(shù)據(jù)，使用日光誘導(dǎo)葉綠素?zé)晒鉁y量系統(tǒng)結(jié)合高光譜設(shè)備采集SIF信號、熒光產(chǎn)量（ΦF）、歸一化植被指數(shù)（NDVI）等數(shù)據(jù)。

研究發(fā)現(xiàn)，冠層尺度上多個熒光相關(guān)指標(biāo)與病情嚴(yán)重度均顯著相關(guān)，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(yōu)于傳統(tǒng)光譜指標(biāo)；而傳統(tǒng)光譜指標(biāo)如NDVI在病害后期的監(jiān)測表現(xiàn)仍具優(yōu)勢。

這意味著SIF信號與傳統(tǒng)光譜指數(shù)具有互補優(yōu)勢，二者結(jié)合可實現(xiàn)更全面、更精準(zhǔn)的小麥條銹病監(jiān)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(qū)，b.冠層光譜測量實驗裝置，c.研究區(qū)小麥的三種形態(tài)

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(guān)系 （SL<20%）。（a–f） 是冠層尺度數(shù)據(jù);（g，h） 是葉尺度數(shù)據(jù)。紅帶內(nèi)的紅線表示回歸線和95%置信區(qū)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(shù)的協(xié)同冠層SIF監(jiān)測冬小麥條銹病

另一研究團(tuán)隊結(jié)合小波能量系數(shù)方法，協(xié)同使用冠層SIF信號，在河北廊坊對冬小麥條銹病進(jìn)行定量監(jiān)測。采用高光譜成像儀采集冠層光譜及葉綠素?zé)晒鈹?shù)據(jù)，深入分析了光譜與熒光信號對病害動態(tài)變化的響應(yīng)。

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(xiàn)，顯著提升了病害檢測的準(zhǔn)確性和時效性。該方法為利用SIF進(jìn)行小麥條銹病動態(tài)監(jiān)控提供了理論和技術(shù)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(yán)重程度下的原始光譜；b.DI 與反射率之間的相關(guān)系數(shù)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(shù)框架 / Methodological framework of the monitoring model for stripe rust

無人機(jī)高光譜成像技術(shù)融合葉綠素?zé)晒庵笜?biāo)實現(xiàn)條銹病早期檢測

該團(tuán)隊還進(jìn)行了另外一組研究：利用無人機(jī)搭載高光譜成像儀，結(jié)合多種色素及相關(guān)光譜指數(shù)，檢測小麥條銹病。該團(tuán)隊通過航拍獲取大范圍田間高光譜數(shù)據(jù)，提取病斑色素特征和光譜指標(biāo)，融合葉綠素?zé)晒庀嚓P(guān)參數(shù)進(jìn)行建模分析。

結(jié)果表明，該方法可實現(xiàn)條銹病的高精度早期檢測，適用于大范圍快速監(jiān)測與病害擴(kuò)散風(fēng)險評估，為農(nóng)業(yè)精準(zhǔn)防控提供可靠技術(shù)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(qū)位置和樣地分布。A表示實驗區(qū)域的位置；B表示無人機(jī)高光譜數(shù)據(jù)采集活動；C表示無人機(jī)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(tài)。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(chǎn)品優(yōu)勢與解決方案

為支持廣泛應(yīng)用，我司自主研發(fā)日光誘導(dǎo)葉綠素?zé)晒?SIF)監(jiān)測系統(tǒng)，具備強大的實時采集能力；同時代理高性能的國產(chǎn)高光譜成像儀，滿足從地面、塔基到無人機(jī)平臺的多場景需求。

用戶可利用這些硬件設(shè)備，自主開發(fā)分析模型，實現(xiàn)小麥條銹病的早期預(yù)警、動態(tài)監(jiān)控與精準(zhǔn)防控，真正實現(xiàn)農(nóng)業(yè)生產(chǎn)的數(shù)字化和智能化轉(zhuǎn)型。

此外，我們的設(shè)備支持集成到農(nóng)業(yè)機(jī)械中，輔助農(nóng)機(jī)實現(xiàn)精準(zhǔn)、智能的高效噴藥作業(yè)，有效提升除病效率，降低農(nóng)藥使用量，推動綠色農(nóng)業(yè)發(fā)展。

歡迎聯(lián)系了解設(shè)備詳情及定制化技術(shù)服務(wù)，讓光譜技術(shù)助力智慧農(nóng)業(yè)，守護(hù)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.