2019
DOI: 10.1016/j.isprsjprs.2019.03.005
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Remote sensing-based crop lodging assessment: Current status and perspectives

Abstract: Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by… Show more

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Cited by 91 publications
(51 citation statements)
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References 139 publications
(200 reference statements)
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“…We proposed a method based on the UAV Timely acquisition of crop lodging grades and area is of great significance for the assessment of yield loss. The development of remote sensing provides an important method to monitor crop lodging [6,62]. However, satellite data can be affected by clouds, the revisiting time is long, and the spatial resolution is low.…”
Section: Discussionmentioning
confidence: 99%
“…We proposed a method based on the UAV Timely acquisition of crop lodging grades and area is of great significance for the assessment of yield loss. The development of remote sensing provides an important method to monitor crop lodging [6,62]. However, satellite data can be affected by clouds, the revisiting time is long, and the spatial resolution is low.…”
Section: Discussionmentioning
confidence: 99%
“…In another study, Chu et al (2017) investigated the potential for lodging detection in maize using crop height estimated from UAV data together with structure-from-motion (SfM) photogrammetry. However, to date the number of RSbased studies characterizing crop lodging using UAV data is limited (Chauhan et al, 2019). Despite the interest, to the best of our knowledge, the sensitivity of multiple spectral bands (9) covering the entire 390-950nm region of the spectrum to crop lodging has not been studied from UAV data.…”
Section: Introductionmentioning
confidence: 99%
“…Liu et al [ 9 ] constructed a model to recognize lodged rice using support vector machine (SVM) and particle swarm optimization (PSO) by analyzing temperature, color and texture information of lodged rice collected from UAV visible and thermal infrared images. Although UAV plays an important role in monitoring rice lodging, the imaging width of UAV is limited to the local or experimental scale; therefore, it cannot meet the requirement of rapid regional-scale monitoring [ 10 ].…”
Section: Introductionmentioning
confidence: 99%