Deep Learning Detection of Weeds in Vegetable Fields

LI Weili; JIN Xiaojun; YU Jialin; CHEN Yong

doi:10.19303/j.issn.1008-0384.2024.02.010

Volume 39 Issue 2

Feb. 2024

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Article Navigation > Fujian Journal of Agricultural Sciences > 2024 > 39(2): 199-205

LI W L, JIN X J, YU J L, et al. Deep Learning Detection of Weeds in Vegetable Fields [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010

Citation:

LI W L, JIN X J, YU J L, et al. Deep Learning Detection of Weeds in Vegetable Fields [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：199−205 doi: 10.19303/j.issn.1008-0384.2024.02.010

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Deep Learning Detection of Weeds in Vegetable Fields

doi: 10.19303/j.issn.1008-0384.2024.02.010

LI Weili^{1, 2
,},
JIN Xiaojun^{2, 3},
YU Jialin³,
CHEN Yong^{2
,
,}

1.
School of Electromechanical Engineering and Automation/Nanhang Jincheng College, Nanjing, Jiangsu　211156, China
2.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu　210037, China
3.
Institute of Advanced Agriculture Sciences, Peking University, Weifang, Shandong　261325, China

Received Date: 2023-07-10
Rev Recd Date: 2023-10-05

Available Online: 2024-03-28

Publish Date: 2024-02-28

Abstract

Abstract

Objective Deep learning to accurately identify weeds for effective weeding in vegetable fields was investigated. Method Image of a vegetable field was cropped into grid cells as sub-images of vegetables, weeds, and bare ground. Deep learning networks using the ShuffleNet, DenseNet, and ResNet models were applied to distinguish the target sub-images, particularly the areas required weeding. Precision, recall rate, F₁ score, and overall and average accuracy in identifying weeds of the models were evaluated. Result Although all applied models satisfactorily distinguished weeds from vegetables, ShuffleNet could simultaneously deliver a 95.5% precision with 97% recall and a highest detection speed of 68.37 fps suitable for real-time field operations. Conclusion The newly developed method using the ShuffleNet model was feasible for precision weed control in vegetable fields.
- Vegetables,
- weeds,
- image treatment,
- deep learning,
- weeding area determination

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References(37)

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