融合卷积神经网络与颜色分割的青菜杂草识别

doi:10.13733/j.jcam.issn.2095‑5553.2024.11.033

摘要/Abstract

摘要： 杂草种类繁多且分布随机导致杂草识别难度大、准确率低和实时性差。为此，提出一种基于深度卷积神经网络的青菜杂草识别方法。首先，利用深度卷积神经网络模型对切分后的网格图像进行青菜识别，以此排除青菜网格图像。进而运用图像处理技术对不包含青菜的网格进行图像分割，识别出不含绿色像素的背景网格图像，并将剩下的网格图像标记为杂草图像。试验结果表明：DenseNet模型、RegNet模型和ShuffleNet模型在测试集识别青菜的总体准确率均高于0.965，展现良好的识别效果。识别速度方面，ShuffleNet模型具有最高的计算效率，识别单张原始图像仅耗时14.12 ms，相应帧率为70.84 fps，满足实时杂草识别应用需求。识别青菜进而区分杂草与土壤，有效降低杂草识别的复杂度，同时提高杂草识别准确率。

关键词: 青菜, 杂草识别, 卷积神经网络, 深度学习, 颜色分割

Abstract: A wide variety and random distribution of weed species made weed identification challenging, resulting in low accuracy and poor real‑time performance. In order to address this issue, a method based on deep convolutional neural networks for identifying spinach weeds was proposed. Initially, a deep convolutional neural network model was used to recognize spinach in segmented grid images, which helped in excluding grid images containing spinach. Subsequently, image processing techniques were applied to segment grid images without spinach, and background grid images lacking green pixels were identified, leaving the remaining grid images marked as weed images. Experimental results showed that the DenseNet model, RegNet model, and ShuffleNet model achieved overall spinach recognition accuracy above 0.965 on the test set, demonstrating excellent identification performance. Regarding recognition speed, the ShuffleNet model exhibited the highest computational efficiency, taking only 14.12 ms to recognize a single original image, corresponding to a frame rate of 70.84 fps, meeting the demands of real‑time weed identification applications. By distinguishing spinach and differentiating weeds from soil, the complexity of weed identification was effectively reduced, while the weed identification accuracy was also enhanced.

Key words: vegetable, weed identification, convolutional neural network, deep learning, color segmentation

中图分类号:

金慧萍, 朱文鹏, 刘腾, 于佳琳, 金小俊, . 融合卷积神经网络与颜色分割的青菜杂草识别[J]. 中国农机化学报, 2024, 45(11): 215-220.

Jin Huiping, Zhu Wenpeng, Liu Teng, Yu Jialin, Jin Xiaojun, . Identification of vegetable weeds by using convolutional neural networks and color segmentation[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(11): 215-220.

参考文献

[ 1 ] 莫开孟, 石丽芬. 新时代背景下我国蔬菜产业发展策略分析[J]. 农村科学实验, 2019(8): 35-36.
Mo Kaimeng, Shi Lifen. Analysis of the development strategy of China's vegetable industry in the context of the new era [J]. Scientific Experiment in Countryside, 2019(8): 35-36.
[ 2 ] 强胜. 我国杂草学研究现状及其发展策略[J]. 植物保护, 2010, 36(4): 1-5.
Qiang Sheng. Current status and development strategy for weed science in China [J]. Plant Protection, 2010, 36(4): 1-5.
[ 3 ] Zhang Y, Staab E S, Slaughter D C, et al. Automated weed control in organic row crops using hyperspectral species identification and thermal micro‑dosing [J]. Crop Protection, 2012, 41: 96-105.
[ 4 ] 李香菊. 近年我国农田杂草防控中的突出问题与治理对策[J]. 植物保护, 2018, 44(5): 77-84.
Li Xiangju. Main problems and management strategies of weeds in agricultural fields in China in recent years [J]. Plant Protection, 2018, 44(5): 77-84.
[ 5 ] 毛文华, 张银桥, 王辉, 等. 杂草信息实时获取技术与设备研究进展[J]. 农业机械学报, 2013, 44(1): 190-195.
Mao Wenhua, Zhang Yinqiao, Wang Hui, et al. Advance techniques and equipments for real‑time weed detection [J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(1): 190-195.
[ 6 ] Saha D, Cregg B M, Sidhu M K. A review of non‑chemical weed control practices in Christmas tree production [J]. Forests, 2020, 11(5): 554.
[ 7 ] Onyango C M, Marchant J A. Segmentation of row crop plants from weeds using colour and morphology [J]. Computers and Electronics in Agriculture, 2003, 39(3): 141-155.
[ 8 ] Cho S I, Lee D S, Jeong J Y. AE‑automation and emerging technologies: Weed‑plant discrimination by machine vision and artificial neural network [J]. Biosystems Engineering, 2002, 83(3): 275-280.
[ 9 ] 陈树人, 沈宝国, 毛罕平, 等. 基于颜色特征的棉田中铁苋菜识别技术[J]. 农业机械学报, 2009, 40(5): 149-152.
Chen Shuren, Shen Baoguo, Mao Hanping, et al. Copperleaf herb detection from cotton field based on color feature [J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(5): 149-152.
[10] Herrmann I, Shapira U, Kinast S, et al. Ground‑level hyperspectral imagery for detecting weeds in wheat fields [J]. Precision Agriculture, 2013, 14: 637-659.
[11] Nieuwenhuizen A T, Hofstee J W, Van Henten E J. Adaptive detection of volunteer potato plants in sugar beet fields [J]. Precision Agriculture, 2010, 11: 433-447.
[12] 邓立苗, 唐俊, 马文杰. 基于图像处理的玉米叶片特征提取与识别系统[J]. 中国农机化学报, 2014, 35(6): 72-75.
Deng Limiao, Tang Jun, Ma Wenjie. Feature extraction and recognition system of maize leaf based on image processing [J]. Journal of Chinese Agricultural Mechanization, 2014, 35(6): 72-75.
[13] García‑Santillán I D, Montalvo M, Guerrero J M, et al. Automatic detection of curved and straight crop rows from images in maize fields [J]. Biosystems Engineering, 2017, 156: 61-79.
[14] Bakhshipour A, Jafari A, Nassiri S M, et al. Weed segmentation using texture features extracted from wavelet sub‑images [J]. Biosystems Engineering, 2017, 157: 1-12.
[15] Bakhshipour A, Jafari A. Evaluation of support vector machine and artificial neural networks in weed detection using shape features [J]. Computers and Electronics in Agriculture, 2018, 145: 153-160.
[16] 王璨, 武新慧, 张燕青, 等. 基于移位窗口Transformer网络的玉米田间场景下杂草识别[J]. 农业工程学报, 2022, 38(15): 133-142.
Wang Can, Wu Xinhui, Zhang Yanqing, et al. Recognizing weeds in maize fields using shifted window Transformer network [J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(15): 133-142.
[17] 孙哲, 张春龙, 葛鲁镇, 等. 基于Faster R-CNN的田间西兰花幼苗图像检测方法[J]. 农业机械学报, 2019, 50(7): 216-221.
Sun Zhe, Zhang Chunlong, Ge Luzheng, et al. Image detection method for broccoli seedlings in field based on Faster R-CNN [J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(7): 216-221.
[18] Hasan A S M M, Sohel F, Diepeveen D, et al. A survey of deep learning techniques for weed detection from images [J]. Computers and Electronics in Agriculture, 2021, 184: 106067.
[19] Meeradevi, Sindhu N, Monica R M. Machine learning in agriculture application: Algorithms and techniques [J]. International Journal of Innovative Technology and Exploring Engineering, 2020, 9(6): 1140-1146.
[20] Osorio K, Puerto A, Pedraza C, et al. A deep learning approach for weed detection in lettuce crops using multispectral images [J]. AgriEngineering, 2020, 2(3): 471-488.
[21] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks [C]. Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
[22] 林海涛, 郑群浩, 林嘉仪, 等. 基于RegNet网络的岩石图像模式识别[J]. 现代信息科技, 2022, 6(14): 63-66.
[23] Zhang X, Zhou X, Lin M, et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6848-6856．
[24] Jin X, Che J, Chen Y. Weed identification using deep learning and image processing in vegetable plantation [J]. IEEE Access, 2021, 9: 10940-10950.
[25] Lee R B. Realtime MPEG video via software decompression on a PA-RISC processor [C]. Digest of Papers. COMPCON'95. Technologies for the Information Superhighway. IEEE, 1995: 186-192.

[1]	赵爽, 俞永强, 苗玉彬, 刘可心 . 基于改进 Mask R-CNN的青菜杂质检测研究[J]. 中国农机化学报, 2024, 45(9): 77-82.
[2]	杨丽, 薛亚许, 李鹏飞, 彭信杰. 基于颜色分割和 PSO-RELM算法的花生种子筛选研究[J]. 中国农机化学报, 2024, 45(9): 89-96.
[3]	韩鹏飞, 宋其江, 贾梦实. 基于改进轻量化 EfficientNet-V2模型的小麦种子分类[J]. 中国农机化学报, 2024, 45(9): 111-117.
[4]	李志臣, 凌秀军, 李鸿秋. 基于声音深度学习的发动机失火故障诊断[J]. 中国农机化学报, 2024, 45(9): 134-140.
[5]	蒲厚旭, 张慧. 基于卷积神经网络干瘪核桃 X射线图像判别[J]. 中国农机化学报, 2024, 45(9): 184-189.
[6]	桑一男 , 徐增莱 , 汪琼 , 葛海涛 , 王殿广 . 一种轮式采花机器人设计——以黄蜀葵为例[J]. 中国农机化学报, 2024, 45(9): 202-208.
[7]	李邦国, 王辉, 宋杨, 任志伟, 刘跃华, 徐乐程. 基于无人驾驶小麦收割机立体视觉感知系统[J]. 中国农机化学报, 2024, 45(9): 244-249.
[8]	张倩, 王明, 于峰, 陶震宇, 张辉, 李刚. 基于CNN的作物分类识别图像获取平台研究进展[J]. 中国农机化学报, 2024, 45(8): 170-179.
[9]	许贞辉, 李晓娟. 果园树干检测与导航线拟合算法研究[J]. 中国农机化学报, 2024, 45(8): 217-222.
[10]	胡国玉, 刘广, 周星光, 董娅兰, 周建平, . 基于Swin-TDL算法的果园环境下葡萄病害检测方法[J]. 中国农机化学报, 2024, 45(8): 234-239.
[11]	黄亦其, 鹿林飞, 沈豪, 王福宽, 乔曦, . 基于卷积神经网络的入侵昆虫识别研究[J]. 中国农机化学报, 2024, 45(7): 222-227.
[12]	吴利刚, 吕媛媛, 周倩, 陈乐, 张梁, 史建华. 基于改进YOLOv5的轻量级黄花成熟检测方法[J]. 中国农机化学报, 2024, 45(7): 235-242.
[13]	张继成, 黄向党. 基于改进YOLOv3的玉米病害识别方法[J]. 中国农机化学报, 2024, 45(7): 269-275.
[14]	雷浩, 苑迎春, 何振学. 基于多尺度卷积和注意力机制的枣品种识别[J]. 中国农机化学报, 2024, 45(6): 135-141.
[15]	崔海朋, 秦朝旭, 马志宇. 基于深度学习的鱼类特征点检测与体征识别方法[J]. 中国农机化学报, 2024, 45(6): 201-207.