A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables

LI Weili; JIN Xiaojun; YU Jialin; CHEN Yong

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LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：1−8

Citation:

LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：1−8

LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：1−8

Citation:

LI W L, JIN X J, YU J L, et al. A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables [J]. Fujian Journal of Agricultural Sciences，2024，39（2）：1−8

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A Deep Learning-based Method for Detection of Precision Weeding Regions in Vegetables

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

1.
Jincheng College of Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu　211156, China
2.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu　210037, China
3.
Peking University Institute of Advanced Agriculture Sciences, Weifang, Shandong　261325, China

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

Available Online: 2024-03-28

Abstract

Abstract

Objective Vegetables grow randomly and often infested with numerous weed species. Traditional algorithms are complex in weed detection, and typically fail to localize the weeding regions precisely. In the study, a deep learning-based method was proposed to identify weeds growing in vegetables in combination with weeding regions detection. Method Firstly, the original images were cropped into grid cells (sub-images). Afterwards, the deep learning models were utilized to classify the grid cells into vegetables, weeds and soil. The grid cells containing weeds were marked as weeding regions. ShuffleNet, DenseNet, and ResNet were employed to detect and classify the grid cells. Deep learning models were evaluated in terms of precision, recall value, F₁ score, and overall and average accuracy. Result The experiment results show that the evaluated deep learning models can identify weeds and vegetables effectively. ShuffleNet model was proven to be the bestmodel, which achieves optimal balance between the precision and recall value, with the values of 95.5% and 97% respectively. Additionly, ShuffleNet achieved the highest detection speed, with the value of 68.37fps, making it suitable for real-time weed detection. Conclusion The results demonstrate that the proposed method is feasible and well-performed, which can be used for precision weeds control in vegetables.
- Vegetables,
- weeds,
- image processing,
- deep learning,
- weeding region detection

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References(36)

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