Planning the temporary takeoff/landing site’s location for a pesticide spraying helicopter based on an intelligent fusion algorithm

Fang, Song; Yu, Ruan; Hu, Chenming; Yang, Fengbo; Xue, Jiangkun; Zhou, Jie

doi:10.1016/j.compag.2023.107826

Cited by 2 publications

(1 citation statement)

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“…Herbicides are typically broadcast‐applied uniformly across the entire field despite the patchy distribution nature of weeds 4 . This approach incurs high input costs and may pollute the environment 5,6 . In Europe, the cost of herbicides represents around 40% of the total expense for all chemicals used in agriculture 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

Jin,

Han,

Zhao

et al. 2024

Pest Management Science

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BACKGROUNDAccurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.RESULTSThe weed detection NNs, including DenseNet, GoogLeNet, and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, the DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing datasets for estimating weed coverage. The inference speed of ResNet was similar to GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network (DCNN) for weed detection and coverage estimation.CONCLUSIONThese results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing the calculation of herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%