基于深度学习的蔬菜田精准除草作业区域检测方法

李卫丽; 金小俊; 于佳琳; 陈勇

doi:10.19303/j.issn.1008-0384.2024.02.010

基于深度学习的蔬菜田精准除草作业区域检测方法

doi: 10.19303/j.issn.1008-0384.2024.02.010

李卫丽^{1, 2,},
金小俊^{2, 3},
于佳琳³,
陈勇^2, ,

1.
南京航空航天大学金城学院机电工程与自动化学院，江苏　南京　211156
2.
南京林业大学机械电子工程学院，江苏　南京　210037
3.
北京大学现代农业研究院，山东　潍坊　261325

基金项目: 国家自然科学基金项目（32072498）；江苏省研究生科研与实践创新计划项目（KYCX22_1051）

详细信息

作者简介:
李卫丽（1985 — ），女，硕士，讲师，主要从事智慧农业、智能机器人技术研究，E-mail：lwl2009seu@126.com

通讯作者:
陈勇（1965 — ），男，教授，博士生导师，主要从事机电一体化研究，E-mail：chenyongjsnj@163.com

中图分类号: TP391.41
计量
- 文章访问数: 312
- HTML全文浏览量: 107
- PDF下载量: 44
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-10
- 修回日期: 2023-10-05
- 网络出版日期: 2024-03-28
- 刊出日期: 2024-02-28

Deep Learning Detection of Weeds in Vegetable Fields

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

摘要

摘要: 目的蔬菜生长随机，杂草种类众多。传统杂草识别算法复杂，且仅识别出杂草，未能精准确定除草作业区域。本研究以蔬菜及其伴生杂草为研究对象，拟探索一种基于深度学习的杂草识别与精准除草作业区域检测方法。方法通过将原图切分网格图像，利用深度学习模型识别蔬菜、杂草及土壤，将包含杂草的网格图像标记为除草作业区域。选取ShuffleNet、DenseNet和ResNet模型开展识别试验，并采用精度、召回率、F₁值和总体准确率、平均准确率分别对验证集和测试集进行评价分析。结果所选的3种网络模型均能较好地识别杂草和蔬菜，其中ShuffleNet为杂草识别最优模型，其对杂草的识别具有较为均衡的精度和召回率，分别为95.5%、97%，且其识别速度也达最优，为68.37 fps，能够应用于实时杂草识别。结论本研究提出的除草作业区域检测方法具有高度的可行性和极佳的识别效果，可用于蔬菜田间杂草的精准防除。
- 蔬菜 /
- 杂草 /
- 图像处理 /
- 深度学习 /
- 作业区域检测
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

HTML全文

图 1 原图切分网格图像

Figure 1. Images of cropped grids

下载: 全尺寸图片幻灯片

图 2 精准除草作业区域检测流程

Figure 2. Flowchart of precision determination on weeding areas

下载: 全尺寸图片幻灯片

图 3 不同深度学习模型的混淆矩阵图

Figure 3. Confusion matrices of deep learning models

下载: 全尺寸图片幻灯片

图 4 精准作业区域检测结果

Figure 4. Precision determination of weeding areas

下载: 全尺寸图片幻灯片

图 5 被识别错误的图片

Figure 5. Incorrectly classified images

下载: 全尺寸图片幻灯片

表 1 深度学习数据集

Table 1. Dataset of deep learning models

样本类别 Sample category	正样本 Positive sample	负样本 Negative sample
样本类别 Sample category	青菜 Vegetable	杂草 Weed	土壤 Soil
训练集 Training dataset	3000	3000	3000
验证集 Validation dataset	500	500	500
测试集 Testing dataset	500	500	500

下载: 导出CSV

表 2 不同模型的默认超参数

Table 2. Hyper-parameters for training convolutional neural networks

模型 Neural network	批尺寸 Batch size	初始学习率 Initial learning rate	学习率调整策略 Learning rate policy	优化器 Optimizer	训练周期 Training epochs
ShuffleNet	16	0.001	LambdaLR	SGD	24
DenseNet	16	0.001	LambdaLR	SGD	24
ResNet	16	0.0001	StepLR	Adam	24

下载: 导出CSV

表 3 不同深度学习模型验证集识别结果

Table 3. Evaluation metrics on validation dataset obtained by deep learning models

模型 Neural Network	类别 Category	精度 P_pre	召回率 P_rec	F₁值 F₁ score
ShuffleNet	土壤 Soil	0.978	0.946	0.967
	青菜 Vegetable	0.990	0.988	0.989
	杂草 Weed	0.955	0.970	0.962
DenseNet	土壤 Soil	0.972	0.974	0.973
	青菜 Vegetable	0.975	0.994	0.984
	杂草 Weed	0.969	0.948	0.958
ResNet	土壤 Soil	0.981	0.946	0.963
	青菜 Vegetable	0.969	0.992	0.980
	杂草 Weed	0.945	0.956	0.950

下载: 导出CSV

表 4 不同深度学习模型测试集评价数据

Table 4. Evaluation metrics on test dataset obtained by deep learning models

模型 Neural network	类别 Category	总体准确率 OAcc	平均准确率 AAcc	网格图像识别速度 Speed of grid cells/fps	原图识别速度 Speed of full images/fps
ShuffleNet	土壤 Soil	0.967	0.951	207.45	68.37
	青菜 Vegetable	0.978
	杂草 Weed	0.957
DenseNet	土壤 Soil	0.967	0.949	104.05	58.94
	青菜 Vegetable	0.979
	杂草 Weed	0.953
ResNet	土壤 Soil	0.962	0.941	289.57	85.42
	青菜 Vegetable	0.977
	杂草 Weed	0.944

下载: 导出CSV

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