Progress and prospects of crop diseases and pests monitoring by remote sensing

doi:10.12133/j.smartag.2019.1.4.201905-SA005

Abstract

Abstract:

Global change and natural disturbances have already caused a severe co-epidemic of crop pests and diseases, such as aphids, fusarium, rust, and powdery mildew. These threats may result in serious deterioration of grain yield and quality. Traditionally, crop pests and diseases are monitored by visual inspection of individual plants, which is time-consuming and inefficient. Besides, the distribution of different infected wheat patches are hard to identify through manual scouting. However, the spatial scale difference of remote sensing observation directly affects the remote sensing diagnosis mechanism and monitoring method of pests and diseases. The differences in pest and disease characterization and monitoring mechanisms promote the development of the remote sensing-based monitoring technology at different spatial scales, and the complementarity of multi-spatial data sources (remote sensing, meteorology, plant protection, etc.) increase the chance of the precision monitoring of the occurrence and development of pest and disease. As a non-destructive way of collecting ground information, remote sensing technologies have been proved to be feasible in crop pests and diseases monitoring and forecasting. Meanwhile, many crop diseases and pests monitoring and alarming systems have been developed to manage and control agricultural practices. Based on the description of physiological mechanism that crop diseases and pests stressed spectral response, some effective spectral wavelengths, remote sensing monitoring technologies, and crop pests and disease monitoring and forecasting system were summarized and sorted in this paper. In addition, challenge problems of key technology on monitoring crop diseases and pests with remote sensing was also pointed out, and some possible solutions and tendencies were also provided. This article detailed revealed the researches on the remote sensing based monitoring methods on detection and classification of crop pests and diseases with the challenges of regional-scale, multi-source, and multi-temporal data. In addition, we also reviewed the remote sensing monitoring of pests and diseases that meet the characteristics of different remote sensing spatial scale data and precise plant protection and control needs. Finally, we investigated the current development of the pest and disease monitoring systems which integrated the research and application of the existing crop pest and disease monitoring and early warning model. In summary, this review will prove a new perspective for sustainable agriculture from the current researches, thus, new technology for earth observation and habitat monitoring will not only directly benefit crop production through better pest and disease management but through the biophysical controls on pest and disease emergence. Application of UAVs, image processing to insect/disease detection and control should be directly transferable to other pests and diseases, with feedbacks into UAV and EO capabilities for the mapping and management of these agricultural risks. Similarly, these vision systems open other possibilities for farm robotics such as mechanical rather than manual pesticide usage for below crop canopy pest surveying.

Key words: crop, remote sensing, pests and diseases, monitoring, future prospects

CLC Number:

Huang Wenjiang, Shi Yue, Dong Yingying, Ye Huichun, Wu Mingquan, Cui Bei, Liu Linyi. Progress and prospects of crop diseases and pests monitoring by remote sensing[J]. Smart Agriculture, 2019, 1(4): 1-11.

Figures/Tables 2

Table 1

The first derivative features, continuum features and vegetation indices used in discrimination of pest and disease

类别	指标	名称	定义	文献
微分光谱	Db	蓝波段一阶微分最大值（蓝边）	蓝边一般分布在490~539nm波段范围	[33]
	λb	Db的波长	λb表征了蓝边 Db处的波长	[33]
	SDb	蓝波段一阶微分光谱的和	表征了蓝边部分35个波段一阶微分光谱的和	[33]
	Dy	黄波段一阶微分最大值（黄边）	黄边一般分布在550~582nm波段范围	[33]
	λy	Dy的波长	λy表征了黄边Dy处的波长	[33]
	SDy	黄波段一阶微分光谱的和	表征了黄边部分35个波段一阶微分光谱的和	[33]
	Dr	红波段一阶微分最大值（红边）	红边一般分布在670~737nm波段范围.	[33]
	λr	Dr的波长	λr表征了红边Dr处的波长	[33]
	SDr	红波段一阶微分光谱的和	表征了红边部分35个波段一阶微分光谱的和	[33]
连续统特征	DEP550-750	光谱深度	波段范围550~750nm	[24]
	DEP920-1120		波段范围920~1120nm	[34]
	DEP1070-1320		波段范围1070~1320nm	[34]
	WID550-750	半波段宽度DEP	波段范围550~750nm	[34]
	WID920-1120		波段范围920~1120nm	[34]
	WID1070-1320		波段范围1070~1320nm	[34]
	AREA550-750	DEP和WID组成区域的面积	波段范围550~750nm	[34]
	AREA920-1120		波段范围920~1120nm	[34]
	AREA1070-1320		波段范围1070~1320nm	[34]
植被指数	GI	绿度指数（Greenness Index）	R ₅₅₄/R ₆₇₇	[35]
	NDVI	归一化植被指数（Normalized Difference Vegetation Index）	(R _NIR-R _R)/(R _NIR+R _R)	[36]
	TVI	三角植被指数（Triangular Vegetation Index）	0.5[120(R ₇₅₀-R ₅₅₀)-200*(R ₆₇₀-R ₅₅₀)]	[37]
	PRI	光化学反射指数（Photochemical Reflectance Index）	(R ₅₇₀-R ₅₃₁)/(R ₅₇₀+R ₅₃₁)	[38]
	CARI	叶绿素吸收率指数（Chlorophyll Absorption Ratio Index）	(\|(a670+R ₆₇₀+b)\|/(a2+1)1/2)*(R ₇₀₀/R ₆₇₀)	[39]
	CARI	叶绿素吸收率指数（Chlorophyll Absorption Ratio Index）	a=(R ₇₀₀-R ₅₅₀)/150,b=R ₅₅₀-(a*550)	[39]
	MCARI	修正的叶绿素吸收率指数(Modified Chlorophyll Absorption Reflectance Index)	[(R ₇₀₀-R ₆₇₀)-0.2(R ₇₀₀-R ₅₅₀)]R ₇₀₀/R ₆₇₀	[21]
	CI_Red-edge	红边叶绿素指数（Red-edge Chlorophyll Index）	(R _NIR/R _E)-1	[40]
	SIPI	结构无关色素指数（Structural Independent Pigment Index）	(R ₈₀₀-R ₄₄₅)/(R ₈₀₀+R ₆₈₀)	[41]
	PSRI	植物衰老反射指数（Plant Senescence Reflectance Index）	(R ₆₇₈-R ₅₅₀)/R ₇₅₀	[41]
	NPCI	归一化叶绿素比值指数（Normalized Pigment Chlorophyll Ratio Index）	(R ₆₈₀-R ₄₃₀)/(R ₆₈₀+R ₄₃₀)	[41]
	OSAVI	优化的土壤调节植被指数（Optimized Soil Adjusted Vegetation Index）	(R _NIR-R _R)/(R _NIR+R _R+0.16)	[42]
	SR	简单比值指数（Simple Ratio Index）	R ₁₆₀₀/R ₈₁₉	[43]
	WI	水份指数（Water Index）	R ₉₀₀/R ₉₇₀	[21]
	NDWI	归一化插值水份指数（Normalized Difference Water Index）	(R ₈₆₀-R ₁₂₄₀)/(R ₈₆₀+R ₁₂₄₀)	[19]
	AI	蚜虫指数（Aphid Index）	(R ₇₄₀-R ₈₈₇)/(R ₆₉₁-R ₆₉₈)	[44]
	GNDVI	绿波段归一化植被指数（Green Normalized Difference Vegetation Index）	(R _NIR-R _G)/(R _NIR+R _G)	[20]
	DSSI2	损伤敏感光谱指数2（Damage Sensitive Spectral Index 2）	(R ₇₄₇-R ₉₀₁-R ₅₃₇-R ₅₇₂)/(R ₇₄₇-R ₉₀₁+R ₅₃₇-R ₅₇₂)	[44]
	HI	健康指数（Healthy Index）	(R ₅₃₄-R ₆₉₈)/(R ₅₃₄+R ₆₉₈)-0.5R ₇₀₄	[12]
	RTVI	定量三角植被指数（Ration Triangular Vegetation Index）	[55(R ₇₅₀-R ₅₇₀)-90(R ₆₈₀-R ₅₇₀)]/[90(R ₇₅₀+R ₅₇₀)]	[45]

Table 1

Table 2

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观测尺度	常用设备	载荷平台	特点及应用	病虫害类型	参考文献
农田地块尺度监测	成像多光谱仪，热红外成像仪	多旋翼无人机，固定翼无人机，传统大飞机	观测范围较大，成本较高，精度较高，以航空飞行器为平台，输出田间病害处方图。	小麦条锈病	[52,53,54,55]
				小麦白粉病	[56,57]
				小麦蚜虫	[58,59]
				小麦黄斑病	[60]
				葡萄黄化病	[61]
				水稻稻飞虱	[62]
				水稻稻瘟病	[63]
				芹菜菌核病	[64]
				番茄潜叶蛾	[65]
				番茄细菌性叶斑病	[66]
				甜菜褐斑病	[67]
区域尺度监测	多光谱卫星（Landsat，GF，Sentinel），高光谱卫星（Hyperion），热红外卫星（Landsat，Aster，HJ）	卫星遥感平台	观测范围极大，成本低，以遥感卫星为数据源，作为大尺度检测和预报提供依据。	小麦条锈病	[68,69]
				小麦白粉病	[70]
				小麦蚜虫	[69]
				水稻稻飞虱	[71]
				水稻矮缩病	[72]
				棉花根腐病	[73]

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