高光谱如何实现柑橘黄龙病的精准防控

Citrus Huanglongbing Terminator: How Hyperspectral Technology Achieves Precise Prevention and Control in Orchards

当超市货架上饱满的脐橙闪烁着诱人的橙红色，很少有人知道这份甜蜜背后潜伏着一场持续百年的“柑橘世界大战"。黄龙病，柑橘产业的头号威胁，被称为柑橘的“癌症"，一旦感染只能砍树。该病症状最早在18世纪被发现，我国1919年开始报告此病。自2021年起，广西柑桔产区连续两年遭受木虱疫害，果园普遍感染黄龙病，导致叶片黄化和果实畸变。

面对叶片黄化、果实畸变的染病果树，传统防控如同“盲人摸象"——人工巡查工作量大效率低，常错过防治时机，而喷洒农药和砍伐病树也未能解决问题。如今，通过高光谱遥感技术，在叶片尚未泛黄时识别黄龙病，提前把握防控窗口期。这串隐藏在光谱波段里的“柑橘密码"，正在重新定义人类与病害的博弈规则。

When plump navel oranges flash their enticing orange-red color on supermarket shelves, few realize that this sweetness is overshadowed by a century-long "World War of Citrus." Huanglongbing (HLB), the number one threat to the citrus industry, is often referred to as the “cancer" of citrus; once infected, the trees must be cut down. The symptoms of this disease were first discovered in the 18th century, and it was first reported in China in 1919. Since 2021, the citrus-growing regions of Guangxi have suffered from severe infestations by the Asian citrus psyllid for two consecutive years, leading to widespread HLB infection that results in yellowing leaves and deformed fruit.

Faced with infected trees exhibiting yellowing leaves and deformities, traditional control methods resemble "blind men trying to touch an elephant"—manual inspections are labor-intensive and inefficient, often missing critical prevention opportunities, while pesticide spraying and tree removal have not effectively resolved the issue. Nowadays, using hyperspectral remote sensing technology, HLB can be identified before the leaves turn yellow, allowing for proactive control measures during the crucial prevention window. This hidden “citrus code" embedded in the spectral bands is redefining the rules of engagement between humans and disease.

当无人机搭载400~1000nm波段的高光谱相机掠过果园，每片叶子都会留下光谱“指纹"。由于患病叶片的光合作用受到抑制，并且含水量降低，其在可见光波段的叶绿素反射区和O-H伸缩振动区与健康叶片之间存在显著差异。

在一些前期验证实验中，某科研团队采集了健康叶片及不同病害程度的柑橘叶片，使用可见-近红外光谱波段进行反射率测量，并重点关注450~800nm区间。经过有效数据筛选，该研究通过最小二乘支持向量机（LS-SVM）和随机森林（RF）算法建立了多种快速分类模型，发现模型的分类准确率分别在92.5%~95%、92.5%~97%。这样的高效检测手段大大提高了病害的早期识别率，为果农提供了及时防治的依据。

When drones equipped with hyperspectral cameras ranging from 400 to 1000 nm fly over orchards, each leaf leaves behind a unique spectral "fingerprint." Infected leaves show significant differences in chlorophyll reflectance in the visible light range and O-H stretching vibration zones compared to healthy leaves due to suppressed photosynthesis and reduced moisture content.

In a series of preliminary validation experiments, a research team collected healthy leaves and leaves with varying levels of disease severity, using the visible-near-infrared spectral range to measure reflectance, focusing primarily on the 450-800 nm range. Following effective data filtering, the study established multiple rapid classification models through least squares support vector machine (LS-SVM) and random forest (RF) algorithms, achieving classification accuracies ranging from 92.5% to 95% and 92.5% to 97%. Such efficient detection methods significantly improved the early identification rate of the disease, providing timely preventive measures for farmers.

健康叶片和患病叶片（不同患病程度）的光谱曲线

Spectral curves of healthy leaves and infected leaves (with varying degrees of disease severity)

华南一个科研团队使用了无人机载高光谱成像系统收集高光谱图像，该高光谱成像仪波长范围是450~950nm，通道数为125。经过光谱预处理和特征工程，应用了连续投影算法（SPA）来提取对柑橘患病植株分类影响最大的特征波长组合，从125个波段中锁定了10个关键波段。这些波段如同破解黄龙病的“摩尔斯电码"，高效地传达了柑橘植株病虫害的信息。

在模型构建方面，研究人员基于全波段数据，应用了BP神经网络和XgBoost算法进行分类评估，同时基于特征波段使用逻辑回归和支持向量机（SVM）算法建立分类模型。

结果显示，基于全波段的BP神经网络和XgBoost算法的分类模型分类准确率均超过95%。基于特征波段（698nm和762nm）的逻辑回归和SVM建立的模型实现了93.00%和96.00%的患病样品分类准确率。这证明了特征波长组合的有效性，为柑橘种植园的病虫害监测和精准防治提供了一定的数据和理论支撑。

A research team in South China utilized a drone-mounted hyperspectral imaging system to collect hyperspectral images, with the hyperspectral imager covering a wavelength range of 450-950 nm and comprising 125 channels. After spectral preprocessing and feature engineering, they applied a successive projection algorithm (SPA) to extract the most influential combinations of spectral wavelengths for classifying infected citrus plants, narrowing down to 10 key bands from 125. These bands resemble the "Morse code" for decoding HLB, efficiently conveying information about the disease and pests affecting citrus plants.

In model construction, researchers applied BP neural networks and XgBoost algorithms for classification assessment based on full-band data, while also using logistic regression and support vector machine (SVM) algorithms to establish classification models based on feature bands.

The results showed that classification models based on the full-band BP neural networks and XgBoost algorithms exceeded 95% classification accuracy. Models based on feature bands (698 nm and 762 nm), developed using logistic regression and SVM, achieved classification accuracies of 93.00% and 96.00% for infected samples. This confirms the effectiveness of the feature wavelength combinations, providing data and theoretical support for pest monitoring and precise prevention in citrus plantations.

华南科研团队的试验区域及样本标注：粉、红、蓝、黄、白圆圈标记分别代表不同患病程度和患病未定级的黄龙病植株，三角形标记为缺素植株；没有标记的植株为健康植株

Experimental area and sample labeling by the research team in South China: The pink, red, blue, yellow, and white circular markers represent citrus plants with different degrees of Huanglongbing severity and those whose condition has not yet been classified; the triangular markers indicate nutrient-deficient plants; unmarked plants are healthy.

中美两所高校展开合作，采用无人机搭载高光谱和多光谱成像系统，获得光谱遥感数据，快速识别感染黄龙病的柑橘植株。该团队将航拍获取的光谱数据与农田和实验室的地面验证结果相结合，显示出航拍的光谱数据能够有效地区分健康植株与感染黄龙病的植株，准确率最高可达90%。

Two universities from China and the United States collaborated using drones equipped with hyperspectral and multispectral imaging systems to obtain spectral remote sensing data for rapid identification of citrus plants infected with HLB. This team combined aerial spectral data with ground-truth validation results from fields and laboratories, demonstrating that aerial spectral data effectively distinguish between healthy plants and those infected with HLB, achieving accuracies of up to 90%.

左图：美国佛罗里达的黄龙病感染区，右图：研究中使用的高光谱与多光谱传感器

Left figure: Huanglongbing infection area in Florida, USA; Right figure: Hyperspectral and multispectral sensors used in the study.

从古诗词中描绘的“柑橘正熟，金果盈枝"的丰收画面，到今天光谱仪中的图谱曲线，人类与作物的对话从未停止。那些舞动的光谱曲线，不仅是科技对抗病害的利剑，更承载着我们对土地最深的敬畏。或许在不远的未来，每颗柑橘都将拥有自己的“光谱证件"——而这，正是科技赋予农业的诗意浪漫。

From ancient poetry depicting the bountiful harvest of “ripe citrus, golden fruits hanging from branches" to the spectral curves represented in spectrometers today, the dialogue between humans and crops has never ceased. Those dancing spectral curves are not only the sword of technology against diseases but also embody our deep respect for the land. Perhaps soon, each citrus fruit will have its own "spectral ID"—a poetic romance of agriculture bestowed by technology.

案例来源 / Source

1.Bai Ziqin, Zhou Changyong,  The Research Progress of Citrus Huanglongbing on Pathogen Diversity and Epidemiology, Chinese Agricultural Science Bulletin, 2012,28(1):133-137.

2.QIU Hong-lin, LIU Tian-yuan, KONG Li-li, YU Xin-na, WANG Xian-da, HUANG Mei-zhen. Rapid Detection of Citrus Huanglongbing Based on Extraction of Characteristic Wavelength of Visible Spectrum and Classification Algorithm[J]. Spectroscopy and Spectral Analysis, 2024,44(6): 1518-1525.

3.Li X, Lee WS, Li M, et al. Spectral difference analysis and airborne imaging classification for citrus greening infected trees Computers and Electronics in Agriculture.. 2012 Apr;83:32-46.

4.DENG Xiaoling, ZENG Guoliang, ZHU Zihao, et al. Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing[J]. Journal of South China Agricultural University, 2020, 41(6): 100-108.

5.Xiuhua Li, Won Suk Lee, Minzan Li, Reza Ehsani, Ashish Ratn Mishra, Chenghai Yang, Robert L. Mangan, Spectral difference analysis and airborne imaging classification for citrus greening infected trees, Computers and Electronics in Agriculture, Volume 83,2012, Pages 32-46.