UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review

被引：62

作者：

Awais M. ^{[1
,7
]}

Li W. ^{[1
]}

Cheema M.J.M. ^{[2
,3
]}

Zaman Q.U. ^{[2
,4
]}

Shaheen A. ^{[5
]}

Aslam B. ^{[6
]}

Zhu W. ^{[7
]}

Ajmal M. ^{[7
]}

Faheem M. ^{[7
,8
]}

Hussain S. ^{[9
]}

Nadeem A.A. ^{[10
]}

Afzal M.M. ^{[11
]}

Liu C. ^{[1
]}

机构：

[1] Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang

[2] Faculty of Agricultural Engineering and Technology, PMAS-Arid Agricultural University, Rawalpindi

[3] NCIB Project, PMAS-Arid Agriculture University, Rawalpindi

[4] Engineering Department, Dalhousie University, Agriculture Campus, Truro, B2N 5E3, NS

[5] Department of Earth Sciences, University of Sargodha, Sargodha

[6] School of Business, Qingdao University, Qingdao

[7] School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang

[8] Department of Farm Machinery and Power, University of Agriculture, Faisalabad

[9] Department of Hydrology and Water Resources, King Abd Ul Aziz University, Jeddah

[10] State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Hubei, Wuhan

[11] Key Lab of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences, University of Chinese Academy of Sciences, Beijing

来源：

International Journal of Environmental Science and Technology | 2023年 / 20卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Crop water stress index; Image processing; Intelligent irrigation; Precision agriculture; Unmanned aerial vehicle (UAV); Vegetation index;

D O I：

10.1007/s13762-021-03801-5

中图分类号：

学科分类号：

摘要：

Water management is becoming a critical issue for sustainable agriculture, especially in the semi-arid region, where problems with water scarcity are rising. More accurate water status recovery in crops is required for precise irrigation through remote sensing technologies. These technologies have a lot of potential in intelligent irrigation because they allow for real-time environmental data collection. Nowadays, digital practices have been used, such as unmanned aerial vehicle (UAV), which plays an essential role in various applications related to crop management. Drones offer an exciting opportunity to track crop fields with high spatial and temporal resolution remote sensing to enhance water stress management in irrigation. Farmers have historically depended on soil moisture measurements and weather conditions to detect crop water status for irrigation scheduling. This review paper summarizes the use of UAV remote sensing data in crops for estimating the water status and gives a detailed summary of the potential capacity of UAV remote sensing for water stress application. The remote sensing techniques help modify agricultural practices to meet this significant challenge by providing repeated information on crop status at different scales and various performances during the season. UAVs successful implementation in water stress estimations depends on UAV features, such as flexibility of use in flight planning, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. UAV with a thermal sensor is considered the most effective technique for detecting water stress using specific indices. Thermal imaging can identify water status variations and crop water stress index (CWSI). This CWSI acquired through UAV thermal sensors imagery can be acceptable for managing real-time irrigation to achieve optimum crop water efficiency. © 2021, Islamic Azad University (IAU).

引用

页码：1135 / 1152

页数：17

共 131 条

[1]

Aasen H., Et al., Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows, Remote Sensing, 10, 7, (2018)

[2]

Agam N., Et al., An insight to the performance of crop water stress index for olive trees, Agric Water Manag, 118, pp. 79-86, (2013)

[3]

Alvino A., Marino S., Remote sensing for irrigation of horticultural crops, Horticulturae, 3, 2, (2017)

[4]

Anderson M.C., Et al., An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US Drought Monitor classifications, J Hydrometeorol, 14, 4, pp. 1035-1056, (2013)

[5]

Awais M., Et al., Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions, Environ Technol Innov, 22, (2021)

[6]

Balota M., Et al., Canopy temperature depression sampling to assess grain yield and genotypic differentiation in winter wheat, Crop Sci, 47, 4, pp. 1518-1529, (2007)

[7]

Baluja J., Et al., Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV), Irrig Sci, 30, 6, pp. 511-522, (2012)

[8]

Beare S., Heaney A., Climate change and water resources in the Murray Darling Basin, Australia, (2002)

[9]

Becker R., World population expected to reach 9.7 billion by 2050, . National Geographic, 31-7, (2015)

[10]

Bellvert J., Et al., Mapping crop water stress index in a ‘Pinot-noir’vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle, Precision Agric, 15, 4, pp. 361-376, (2014)

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