吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (8): 2421-2429.doi: 10.13229/j.cnki.jdxbgxb.20211070

• 农业工程·仿生工程 • 上一篇    

基于深度学习与图像处理的蔬菜苗期杂草识别方法

金小俊1(),孙艳霞2,于佳琳3,陈勇1()   

  1. 1.南京林业大学 机械电子工程学院, 南京 210037
    2.南京交通职业技术学院 轨道交通学院, 南京 211188
    3.北京大学 现代农业研究院, 山东 潍坊 261325
  • 收稿日期:2021-10-18 出版日期:2023-08-01 发布日期:2023-08-21
  • 通讯作者: 陈勇 E-mail:xiaojun.jin@outlook.com;chenyongjsnj@163.com
  • 作者简介:金小俊（1987-），男，工程师，博士研究生.研究方向：机器视觉与人工智能技术.E-mail：xiaojun.jin@outlook.com
  • 基金资助:
    国家自然科学基金项目(32072498);江苏省重点研发计划项目(BE2021016);江苏省农业科技自主创新项目（CX（21）3184）

Weed recognition in vegetable at seedling stage based on deep learning and image processing

Xiao-jun JIN1(),Yan-xia SUN2,Jia-lin YU3,Yong CHEN1()   

  1. 1.College of Mechanical and Electronic Engineering，Nanjing Forestry University，Nanjing 210037，China
    2.School of Rail Transportation，Nanjing Vocational Institute of Transport Technology，Nanjing 211188，China
    3.Institute of Advanced Agricultural Sciences，Peking University，Weifang 261325，China
  • Received:2021-10-18 Online:2023-08-01 Published:2023-08-21
  • Contact: Yong CHEN E-mail:xiaojun.jin@outlook.com;chenyongjsnj@163.com

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摘要：

对苗期青菜及其伴生杂草进行识别试验，提出了一种基于识别蔬菜进而间接识别杂草的独特方法，该方法结合深度学习和图像处理技术，可以有效降低杂草识别的复杂度，同时提高识别的精度和鲁棒性。首先，采用神经网络模型对青菜进行识别，并标记边框；然后，将青菜边框之外的绿色目标视为杂草，利用颜色特征将其分割，并通过面积滤波得到滤除噪点后的杂草区域；为探究不同深度学习模型对青菜识别的效果，选取SSD模型、RetinaNet模型和FCOS模型，以F1值、平均精度和检测速度3个评价指标进行对比分析。结果表明，SSD模型为青菜识别最优模型，拥有最高的检测速度和较优的识别率，其在测试集的F1值、平均精度和检测速度分别为95.4%、98.1%和31.0 f/s；改进后的MExG因子能有效识别杂草，分割后的杂草形态完整且轮廓清晰。本文提出的蔬菜田杂草识别方法具有高度的可行性和极佳的应用效果，可为相似作物田杂草识别提供技术参考。

关键词: 农业工程, 蔬菜识别, 杂草识别, 深度学习, 图像处理, 颜色特征

Abstract:

In this study， the recognition test of bok choy and its associated weeds at the seedling stage was carried out， and a novel method based on recognizing vegetables and then indirectly recognizing weeds was proposed. By combining deep learning and image processing technology， this method can effectively reduce the complexity of weed recognition， and at the same time improving the accuracy and robustness of weed recognition. First， a neural network model was used for detecting the bok choy and drawing bounding boxes. The green targets outside the bok choy bounding boxes were marked as weeds， and color features were used to segment them. Besides， an area filter was used for eliminating the noises and extracting weed regions. In order to explore the effects of different deep learning models on bok choy recognition， SSD model， RetinaNet model and FCOS model were selected， and three evaluation metrics of F1 value， average accuracy and detection speed were used for comparative analysis. The SSD model was the best model for bok choy recognition， with the highest detection speed and excellent recognition rate. Its F1 value， average accuracy and detection speed in the test set were 95.4%， 98.1% and 31.0 f/s， respectively. The improved MExG index can effectively recognize weeds， and the segmented weeds have complete shapes and clear outlines. Experiment results show that the proposed method for recognizing weeds in vegetable fields is highly feasible and has excellent application effects， which can also provide technical reference for weed recognition in similar crop fields.

Key words: agricultural engineering, vegetable recognition, weed recognition, deep learning, image processing, color feature

中图分类号: 

  • TP391.41

图1

杂草识别流程示意图"

表1

不同模型超参设置"

模型批尺寸动量初始学习率/10-3优化器衰减值/10-4训练周期
SSD40.92SGD524
RetinaNet40.910SGD124
FCOS40.910SGD124

表2

验证集不同置信度阈值的评价数据"

模型置信度精度召回率F1值
SSD0.90.9800.840.904
0.80.9710.890.929
0.70.9660.910.937
0.60.9640.940.952
0.50.9600.950.955
0.40.9590.960.960
0.30.9410.970.955
0.20.9220.980.950
0.10.8950.990.940
RetinaNet0.90.9670.950.958
0.80.9580.960.959
0.70.9580.960.959
0.60.9400.970.955
0.50.9240.980.951
0.40.9240.980.951
0.30.9240.980.951
0.20.8870.990.936
0.10.8870.990.936
FCOS0.90.0000.000.000
0.81.0000.060.113
0.71.0000.280.438
0.60.9980.510.675
0.50.9910.740.847
0.40.9820.870.922
0.30.9700.930.949
0.20.9110.960.935
0.10.8500.970.906

表3

测试集最优置信度阈值的评价数据"

模型置信度精度召回率F1 值平均精度检测速度/（f·s-1）
SSD0.40.9380.970.9540.98131.0
RetinaNet0.7/0.80.943/0.9470.98/0.970.961/0.9590.98417.5
FCOS0.30.9700.940.9550.96617.9

图2

不同场景下的原图及SSD模型青菜检测效果图"

图3

漏检和误检场景图"

图4

杂草分割效果图"

图5

面积滤波及最终杂草识别效果图"

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