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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)

Abstract:

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"

作物病害类型
Type of crop diseases
光谱响应波段
Spectral response band (nm)
特征选择算法
Feature selection algorithm
参考文献
References
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]
小麦条锈病、小麦白粉病
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"

模型
Model
作物病害种类
Type of crop diseases
算法
Algorithm
文献
Reference
统计模型
Statistical model
小麦白粉病
Wheat powdery mildew
相关分析、方差分析
Correlation analysis and variance analysis
[41]
小麦条锈病
Wheat stripe rust
线性回归、非线性回归
Linear regression and nonlinear regression
[42]
小麦白粉病
Wheat powdery mildew
Logistic回归
Logistic regression
[43]
番茄叶斑病
Tomato bacterial spot
偏最小二乘回归、多元逐步回归
Partial least squares regression and multiple stepwise regression
[44]
小麦条锈病
Wheat stripe rust
偏最小二乘法
Partial least squares
[9]
人工智能模型
Artificial
intelligence model
水稻颖枯病、曲霉病
Rice glume blight disease and false smut disease
主成分分析
Principal component analysis
[31]
黄瓜花叶病毒
Cucumber mosaic virus
人工神经网络
Artificial neural network
[45]
小麦白粉病
Wheat powdery mildew
Fisher线性判别分析、AdaBoost和支持向量机
Fisher linear discriminant analysis, support vector machine, and AdaBoost model
[46]
大豆枯萎病
Soybean sudden death syndrome
偏最小二乘判别分析
Partial least squares discriminant analysis
[47]
油棕茎腐病
Oil palm basal stem rot
决策树、随机森林和支持向量机
Decision tree, random forest, and support vector machine
[48]
小麦白粉病
Wheat powdery mildew
随机森林
Random forest
[49]
蚕豆病虫害
Broad bean disease and pests
聚类算法
K-Means and the FCM clustering
[50]

Table 3

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

监测尺度
Monitoring scale
设备
Devices
特点
Characteristics
病害类型
Type of diseases
参考文献
Reference
叶片及冠层尺度
Leaf and canopy scale
非成像高光谱扫描仪、成像高光谱仪。
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.
甜菜叶斑病、叶锈病和白粉病
Sugar beet leaf spot, leaf rust and powdery mildew
[66]
小麦白粉病
Wheat powdery mildew
[67]
地块尺度
Plot scale
成像多光谱仪、成像高光谱相机、热红外成像仪。
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.
水稻穗瘟病
Rice panicle blast
[26]
番茄晚疫病
Tomato late blight
[68]
区域尺度
Regional scale
多光谱卫星、高光谱卫星、热红外卫星。
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.
小麦锈病
Wheat rust
[69]
小麦白粉病
Wheat powdery midew
[70]
小麦黄锈病
Wheat yellow rust
[71]
芦笋紫斑病
Asparagus purple spot disease
[72]
黄锈病和蚜虫
Wheat yellow rust and aphid
[73]

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"

反演算法
Retrieval algorithms
Fraunhofer线内外反射率和荧光关系
The relationship of reflectance and SIF between the internal and external Fraunhofer dark line respectively
参考文献
Reference
单波段
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]
全波段
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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