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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (11): 2067-2079.doi: 10.3724/SP.J.1006.2021.03057

• REVIEW •     Next Articles

Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data

JING Xia1(), ZOU Qin1, BAI Zong-Fan1, HUANG Wen-Jiang2,*()   

  1. 1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
    2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2020-09-29 Accepted:2021-04-26 Online:2021-11-12 Published:2021-05-21
  • Contact: HUANG Wen-Jiang E-mail:jingxiaxust@163.com;huangwj@aircas.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41601467);National Natural Science Foundation of China(52079103)


Crop diseases are biological disasters that affect grain production and quality. The infestation of diseases consumes the nutrients and water, disrupts its normal life process, and causes changes in the internal physiological and biochemical state and external appearance of the crop. Canopy reflectance spectrum can detect crop population structure information well, and chlorophyll fluorescence data can sensitively reflect changes in crop photosynthetic physiology, both methods are capable of detecting crop diseases via remote sensing technology. This article outlined the current research status of crop diseases detection based on reflectance spectrum through remote sensing technology from the aspects of monitoring methods and monitoring scales, summarized the research progress of using active fluorescence, passive fluorescence and coordinated solar-induced chlorophyll fluorescence and reflectance spectroscopy to monitor crop diseases, analyzed the advantages and disadvantages of reflectance spectrum and chlorophyll fluorescence data in crop disease early warning detection, and discussed the possible problems in the remote sensing detection of crop diseases. On the basis, we made a prospect for the development of remote sensing monitoring crop diseases. This paper provides an important reference for the subsequent applications of crop diseases detection based on reflectance spectrum and chlorophyll fluorescence data.

Key words: reflectance, chlorophyll fluorescence, crop diseases, remote sensing monitoring

Table 1

Feature selection algorithm and sensitive band"

Type of crop diseases
Spectral response band (nm)
Feature selection algorithm
小麦条锈病 Wheat stripe rust 560-670 Filter [24]
小麦白粉病 Wheat powdery mildew 490, 510, 516, 540, 780, 1300 Filter [25]
水稻穗颈瘟 Rice panicles blast 430-530, 580-680, 1480-2000 Filter [26]
番茄晚疫病 Tomato late blight 700-750, 750-930, 950-1030, 1040-1130 Filter [27]
棉花黄萎病 Cotton verticillium wilt 680-760, 731-1371 Filter [28]
玉米大斑病 Corn leaf blight 725-740 Filter [29]
Wheat yellow rust, wheat powdery mildew
480, 633, 934 Wrapper [30]
水稻颖枯病 Rice panicles 450-850 Wrapper [31]
番茄叶斑病 Tomato bacterial spot 395, 633-635, 750-760 Wrapper [32]
花生叶斑病 Peanut leaf spots 761, 938 Wrapper [33]
苹果黑星病 Venturia inaequalis infection 1350-1750, 2200-2500 Embedded [34]
马铃薯晚疫病 Potato late blight 600-900 Embedded [35]

Table 2

Remote sensing monitoring algorithm for crop diseases"

Type of crop diseases
Statistical model
Wheat powdery mildew
Correlation analysis and variance analysis
Wheat stripe rust
Linear regression and nonlinear regression
Wheat powdery mildew
Logistic regression
Tomato bacterial spot
Partial least squares regression and multiple stepwise regression
Wheat stripe rust
Partial least squares
intelligence model
Rice glume blight disease and false smut disease
Principal component analysis
Cucumber mosaic virus
Artificial neural network
Wheat powdery mildew
Fisher linear discriminant analysis, support vector machine, and AdaBoost model
Soybean sudden death syndrome
Partial least squares discriminant analysis
Oil palm basal stem rot
Decision tree, random forest, and support vector machine
Wheat powdery mildew
Random forest
Broad bean disease and pests
K-Means and the FCM clustering

Table 3

Application cases of remote sensing monitoring crop diseases at different scales"

Monitoring scale
Type of diseases
Leaf and canopy scale
Non-imaging hyperspectral scanner and imaging hyperspectral spectrometer.
方便、灵活以及受外界因素影响较小、监测精度高, 通常用于作物病害的遥感探测机理研究, 受探测范围限制, 难以实现大区域作物病害的遥感探测。
Convenient, flexible and less affected by external factors, with high detection accuracy. It is usually used to study the mechanism of early warning and detection of crop diseases. Due to the limitation of the detection range, it is difficult to realize the correction and detection of large-area crop diseases.
Sugar beet leaf spot, leaf rust and powdery mildew
Wheat powdery mildew
Plot scale
Imaging multi-spectrometer, imaging hyperspectral camera and thermal infrared imager.
通常利用搭载于航空平台的传感器监测, 探测范围较大, 数据源获取相对航天数据更为灵活且受天气状况影响较小, 对爆发性流行病害具有一定的应急监测能力。
Usually, the sensors carried on the aviation platform are used for monitoring, which has a large detection range, more flexible data source acquisition compared with space data, and less affected by weather conditions, and have a certain emergency monitoring ability for explosive epidemic diseases.
Rice panicle blast
Tomato late blight
Regional scale
Multispectral satellites, hyperspectral satellites and thermal infrared satellites.
探测范围广, 以卫星数据为数据源, 能周期性地对同一地区进行重复监测, 为大尺度病害预报和流行趋势提供依据。
Wide detection range. Using satellite data as a data source, it can periodically re-monitor the same area and provide a basis for large-scale disease forecasts and epidemic trends.
Wheat rust
Wheat powdery midew
Wheat yellow rust
Asparagus purple spot disease
Wheat yellow rust and aphid

Fig. 1

Release path of absorbed light energy in leaves under steady-state conditions and conceptual figure of leaf fluorescence emission[79]"

Fig. 2

Effect of narrow atmospheric absorption zones on solar irradiance (left) and filling effect of fluorescence emission on absorption zone (right)[104]"

Table 4

SIF extraction algorithm for single spectrum and full spectrum"

Retrieval algorithms
The relationship of reflectance and SIF between the internal and external Fraunhofer dark line respectively
Single spectrum
FLD r(λout) = r(λin), F(λout) = F(λin) [102]
3FLD r(λin) = r(λleftωleft+r(λrightωright, F(λout) = F(λin) [103]
iFLD r(λout) = αR r(λin), F(λout) = αFF(λin) [104]
SFM r(λ) = f(rλ), F(λ) = f(Fλ) [106]
pFLD $\ddot{R}(\lambda )=\sum\limits_{i=1}^{n}{{{k}_{i}}{{\phi }_{i}}(\lambda )} $ [105]
Full spectrum
SFM r(λ) = S(rλ), F(λ) = V(Fλ) [107]
FSR F(λ) ≈ b0+b1∙(λ-λ0)+b2∙(λ-λ0)², r(λ) ≈ b3+b4∙(λ-λ0)+b5∙(λ-λ0 [108]
F-SFM $r(\lambda )=\sum\limits_{i}^{m}{{{k}_{i}}{{\varphi }_{i}}}(\lambda )$., $F(\lambda)=\sum_{i}^{n} j_{i} {\phi }_{i}(\lambda)$ [109]
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