Using crop fall patterns to provide an insight into thunderstorm downbursts

Sterling, Mark; Huo, Shuan; Baker, Chris

doi:10.1016/j.jweia.2023.105431

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…The current model is deliberately tailored for forested regions, excluding suburban and crop areas [53]. Ongoing efforts involve implementing a comparable algorithm with a different deep-learning model designed for these specific scenarios.…”

Section: Discussionmentioning

confidence: 99%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

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Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.

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Section: Discussionmentioning

confidence: 99%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

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“…The leaves are approximated as rectangles, so they can be regarded as cantilever beams with a rectangular section ( Sterling et al., 2023 ). Taking the root of weed leaves as the origin, the plane parallel to the soil as the x-axis, the growth direction of leaves as the positive direction of the x-axis, and the growth direction of plants as the positive direction of the y-axis, a coordinate system of weed leaves is established.…”

Section: Methodsmentioning

confidence: 99%

An intelligent spraying system for weeds in wheat fields based on a dynamic model of droplets impacting wheat leaves

Xie,

Song,

Wen

et al. 2024

Front. Plant Sci.

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IntroductionTargeted herbicide application refers to precise application of herbicides in weed-infested areas according to the location and density of farmland weeds. At present, targeted herbicide application in wheat fields generally faces problems including the low herbicide adhesion rate, leading to omission and excessive loss of herbicides.MethodsTo solve these problems, changes in the impact force of herbicide and the weed leaves in the operation process of a spraying system were studied from the interaction between weeds and herbicides applied. A dynamic model of weed leaves was established. On this basis, the research indicated that the herbicide adhesion rate is highest under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s when the spray height is 300 mm. To study the dynamic deformation of weed leaves and the distribution of liquid herbicides in the external flow field under weed-herbicide interaction, a dynamic simulation model of herbicide application was built using the finite element method.Results and DiscussionThe results show that when the spray height is 300 mm, the maximum weed leaf deformation index (LDI) is 0.43 and the velocity in the external flow field is 0 m/s under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s. This finding indicates that the herbicide is not splashed elsewhere and the turbulence intensity in the weed area is 2%, implying steady flow of the herbicide, most of which can be retained on weed leaves. Field test results of application quality of the herbicide show that the maximum LDI is 0.41 and the coverage of the herbicide in the sheltered area below the leaves is 19.02% when the spraying pressure is 0.4 MPa, flow rate is 0.011 kg/s, and spray height is 300 mm. This solves the problem of a low rate of utilization of herbicides because the herbicide passes through weed plants, and achieves the precision herbicide application in wheat fields.

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Using crop fall patterns to provide an insight into thunderstorm downbursts

Cited by 2 publications

References 34 publications

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

An intelligent spraying system for weeds in wheat fields based on a dynamic model of droplets impacting wheat leaves

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