Lawn weed localization based on semantic segmentation and fusion algorithms

Guo Peng; Li Wenbin; Xu Daochun; Bai Xiaopeng; Wang Ziyun

doi:10.12171/j.1000-1522.20240057

Journal of Beijing Forestry University > 2024 > 46(7): 133-138. > DOI: 10.12171/j.1000-1522.20240057

Guo Peng, Li Wenbin, Xu Daochun, Bai Xiaopeng, Wang Ziyun. Lawn weed localization based on semantic segmentation and fusion algorithms[J]. Journal of Beijing Forestry University, 2024, 46(7): 133-138. DOI: 10.12171/j.1000-1522.20240057

Citation:

PDF (2119 KB)

Lawn weed localization based on semantic segmentation and fusion algorithms

Key Laboratory of National Forestry Administration on Forestry Equipment and Automation, School of Technology, Beijing Forestry University, Beijing 100083, China

More Information

Received Date: March 09, 2024
Revised Date: June 18, 2024
Accepted Date: June 23, 2024
Available Online: June 25, 2024

Graphical Abstract

Abstract

Abstract

Objective
The precise location algorithm of lawn weeds based on a fusion algorithm was explored to provide theoretical basis and technical support for lawn weed automatic identification and removal robots to carry out weeding operations.
Method
A multi-method fusion algorithm based on semantic segmentation for lawn location was proposed. First, the PSPNet network segmented the lawn and non-lawn contours. Secondly, for the segmented non-lawn contours, the interest area was extracted, excluding the non-weed contours and retaining the weed contours. Then, Zhang-Suen thinning algorithm extracted weed contour skeleton lines and obtained the number and coordinates of skeleton intersections. Finally, The fusion algorithm selected different positioning strategies according to the number of intersecting points to achieve accurate positioning of weed roots.
Result
The root-mean-square error between the location coordinates of weeds by fusion algorithm and the actual root center coordinates of weeds was 83.17 Px, and reduced by 14% compared with the method of mean centroid, reduced by 22% compared with the method of a minimum circumscribed circle. Converted to the actual scenario, the root-mean-square error between the location coordinates of weeds by fusion algorithm and the actual root center coordinates of weeds was 12.48 mm, which was within the acceptable error range. The fusion algorithm can improve the accuracy of weed root center location, and reduce the error of weed location.
Conclusion
The location method of lawn weeds based on semantic segmentation and fusion algorithm can improve the localization accuracy of weed root centers, reduce the positioning error by a single method, and provide technical support for automatic identification and removal of lawn weeds.

FullText(HTML)

References (23)

References

[1]	黄玉梅, 王若然, 罗智丹, 等. 模拟酸雨和除草剂单一及复合胁迫对城市草坪土壤动物的影响[J]. 浙江农业学报, 2019, 31(12): 2095−2108. doi: 10.3969/j.issn.1004-1524.2019.12.19 Huang Y M, Wang R R, Luo Z D, et al. Effects of simulated acid rain and herbicide single and compound stress on soil fauna in urban lawn[J]. Acta Agriculturae Zhejiangensis, 2019, 31(12): 2095−2108. doi: 10.3969/j.issn.1004-1524.2019.12.19
[2]	伍同, 曾山, 赵润茂, 等. 智能化作物株间机械除草技术分析与研究[J]. 农机化研究, 2019, 41(6): 1−6, 12. doi: 10.3969/j.issn.1003-188X.2019.06.001 Wu T, Zeng S, Zhao R M, et al. Analysis and study on intelligentialized technologies of intra-row weeding mechanical control[J]. Journal of Agricultural Mechanization Research, 2019, 41(6): 1−6, 12. doi: 10.3969/j.issn.1003-188X.2019.06.001
[3]	Li Y, Guo Z, Shuang F, et al. Key technologies of machine vision for weeding robots: a review and benchmark[J]. Computers and Electronics in Agriculture, 2022, 196: 106880. doi: 10.1016/j.compag.2022.106880
[4]	Yu J, Sharpe S M, Schumann A W, et al. Detection of broadleaf weeds growing in turfgrass with convolutional neural networks[J]. Pest Management Science, 2019, 75(8): 2211−2218. doi: 10.1002/ps.5349
[5]	Tang J L, Wang D, Zhang Z G, et al. Weed identification based on K-means feature learning combined with convolutional neural network[J]. Computers and Electronics in Agriculture, 2017, 135: 63−70. doi: 10.1016/j.compag.2017.01.001
[6]	陈亚军, 赵博, 李树君, 等. 基于多特征的杂草逆向定位方法与试验[J]. 农业机械学报, 2015, 46(6): 257−262. doi: 10.6041/j.issn.1000-1298.2015.06.037 Chen Y J, Zhao B, Li S J, et al. Weed reverse positioning method and experiment based on multi-feature[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(6): 257−262. doi: 10.6041/j.issn.1000-1298.2015.06.037
[7]	Jin X, Che J, Chen Y. Weed identification using deep learning and image processing in vegetable plantation[J]. IEEE Access, 2021, 9: 10940−10950. doi: 10.1109/ACCESS.2021.3050296
[8]	李春明, 逯杉婷, 远松灵, 等. 基于Faster R-CNN的除草机器人杂草识别算法[J]. 中国农机化学报, 2019, 40(12): 171−176. Li C M, Lu S T, Yuan S L, et al. Weed identification algorithm of weeding robot based on faster R-CNN[J]. Journal of Chinese Agricultural Mechanization, 2019, 40(12): 171−176.
[9]	Babiker I, Xie W F, Chen G. Recognition of dandelion weed via computer vision for a weed removal robot[C]//2019 1st International Conference on Industrial Artificial Intelligence (IAI). Shenyang: IEEE, 2019: 1−6.
[10]	Knoll F J, Czymmek V, Poczihoski S, et al. Improving efficiency of organic farming by using a deep learning classification approach[J]. Computers and Electronics in Agriculture, 2018, 153: 347−356. doi: 10.1016/j.compag.2018.08.032
[11]	毛文华, 王辉, 赵博, 等. 基于株心颜色的玉米田间杂草识别方法[J]. 农业工程学报, 2009, 25(增刊2): 161−164. Mao W H, Wang H, Zhao B, et al. Weed detection method based the centre color of corn seedling[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(Suppl. 2): 161−164.
[12]	戚澍, 仉子赫, 张博洋. 基于改进USE-Net网络的林木图像语义分割研究[J]. 森林工程, 2022, 38(6): 82−87. doi: 10.3969/j.issn.1006-8023.2022.06.011 Qi S, Zhang Z H, Zhang B Y. Research on tree image semantic segmentation based on improved USE-Net network[J]. Forest Engineering, 2022, 38(6): 82−87. doi: 10.3969/j.issn.1006-8023.2022.06.011
[13]	张博洋, 倪海明, 胡馨月, 等. 基于U-Net网络的林木图像分割研究[J]. 森林工程, 2021, 37(2): 67−73. doi: 10.3969/j.issn.1006-8023.2021.02.010 Zhang B Y, Ni H M, Hu X Y, et al. Research on tree image segmentation based on U-Net network[J]. Forest Engineering, 2021, 37(2): 67−73. doi: 10.3969/j.issn.1006-8023.2021.02.010
[14]	权龙哲, 肖云瀚, 王建宇, 等. 智能除草装备苗草模式识别方法研究[J]. 东北农业大学学报, 2018, 49(9): 79−87. Quan L Z, Xiao Y H, Wang J Y, et al. Study on pattern recognition method of intelligent weeding equipment[J]. Journal of Northeast Agricultural University, 2018, 49(9): 79−87.
[15]	魏帅均, 张彦娥, 梅树立. 基于俯视图像的玉米株心快速识别方法研究[J]. 农业机械学报, 2017, 48(增刊1): 136−141. doi: 10.6041/j.issn.1000-1298.2017.S0.022 Wei S J, Zhang Y E, Mei S L. Fast recognition method of maize core based on top view image[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(Suppl. 1): 136−141. doi: 10.6041/j.issn.1000-1298.2017.S0.022
[16]	蒋郁, 崔宏伟, 区颖刚, 等. 基于茎基部分区边缘拟合的稻株定位方法[J]. 农业机械学报, 2017, 48(6): 23−31, 49. doi: 10.6041/j.issn.1000-1298.2017.06.003 Jiang Y, Cui H W, Ou Y G, et al. Positioning method of rice seedlings based on edge fitting in separated region of stem base[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(6): 23−31, 49. doi: 10.6041/j.issn.1000-1298.2017.06.003
[17]	宗泽, 赵硕, 刘刚. 苗期玉米冠层识别与质心定位方法研究[J]. 农业机械学报, 2019, 50(增刊1): 27−33. doi: 10.6041/j.issn.1000-1298.2019.S0.005 Zong Z, Zhao S, Liu G. Coronal identification and centroid location of maize seedling stage[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(Suppl. 1): 27−33. doi: 10.6041/j.issn.1000-1298.2019.S0.005
[18]	陈民慧. 面向低对比度的作物杂草分割定位研究[D]. 合肥: 安徽农业大学, 2023. Chen M H. Research on segmentation and localization of crop and weed under low contrast[D]. Hefei: Anhui Agricultural University, 2023.
[19]	文朝武, 龙洁花, 张宇, 等. 基于3D 视觉的番茄授粉花朵定位方法[J]. 农业机械学报, 2022, 53(8): 320−328. Wen C W, Long J H, Zhang Y, et al. Positioning method of tomato pollination flowers based on 3D vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(8): 320−328.
[20]	Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2881−2890.
[21]	Woebbecke D M, Meyer G E, von Bargen K, et al. Color indices for weed identification under various soil, residue, and lighting conditions[J]. Transactions of the ASAE, 1995, 38(1): 259−269. doi: 10.13031/2013.27838
[22]	Saeed K, Tabędzki M, Rybnik M, et al. K3M: a universal algorithm for image skeletonization and a review of thinning techniques[J]. International Journal of Applied Mathematics and Computer Science, 2010, 20(2): 317−335. doi: 10.2478/v10006-010-0024-4
[23]	Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns[J]. Communications of the ACM, 1984, 27(3): 236−239. doi: 10.1145/357994.358023

Cited By

Cited by

Periodical cited type(6)

1.	崔恒久，曾教科，李雯，黄海杰，彭世清，郭冬，蒲金基，周永凯，李辉亮. 腰果CBF基因家族的鉴定及响应冷胁迫的表达模式. 热带作物学报. 2024(03): 473-480 .
2.	韩立群，张捷，赵钰，梅闯，马凯. 新疆野生核桃JfDREB1A基因的克隆与原核表达分析. 西北农业学报. 2023(07): 1050-1057 .
3.	赵淑玲，王永斌，蹇小勇，郭兴贵，朱福民，杨静静，霍发喜. 平欧杂交榛研究进展. 现代化农业. 2023(09): 46-49 .
4.	夏蕴，巢建国，谷巍，盛业龙，王玉卓，惠西珂，王凯，王圆圆. 倒春寒胁迫与恢复对茅苍术生长、生理及关键酶基因的影响. 中成药. 2020(08): 2187-2191 .
5.	宋静武，彭磊. 核桃CBF基因在低温胁迫中表达. 湖北林业科技. 2019(02): 7-13 .
6.	刘晓丹，王霞，杨祥波. 非生物胁迫下山腊梅CpCBF基因的表达模式分析. 黑龙江农业科学. 2017(12): 18-21 .

Other cited types(3)

Get Citation

PDF

XML

Article views (252) PDF downloads (44) Cited by(9)

Turn off MathJax

Article Contents

Abstract

References

Lawn weed localization based on semantic segmentation and fusion algorithms

Abstract

References

Cited by

Periodical cited type(6)

Other cited types(3)

Catalog

Export File

Citation

Format

Content