Deep learning-based weed detection for precision herbicide application in turf.

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Tác giả: Yong Chen, Kang Han, Xiaojun Jin, Xiaotong Kong, Jinglin Lei, Jialin Yu, Hua Zhao, Qiuyu Zu

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: England : Pest management science , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 734863

BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasibility of implementing herbicide susceptibility-based weed mapping using deep convolutional neural networks (DCNNs) to facilitate targeted and efficient herbicide applications. Additionally, applying path-planning algorithms to weed mapping data to guide the spraying nozzle ensures minimal travel paths for herbicide application. RESULTS: DenseNet achieved high precision, recall, overall accuracy, and F CONCLUSION: Implementing herbicide susceptibility-based weed mapping facilitates targeted herbicide application by directing the nozzle to the grid cells containing the weeds susceptible to the herbicides. Moreover, the strategic integration of herbicide susceptibility-based weed mapping with optimized path planning for the spraying mechanism can be adeptly implemented on smart sprayers, which could effectively reduce the herbicide input. © 2025 Society of Chemical Industry.
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