- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
WAN Guang, CHEN Zhonghui, FANG Hongbo, et al. Classification of fresh tea leaf based on random forest model by feature fusion[J]. Journal of South China Agricultural University, 2021, 42(4): 125-132. DOI: 10.7671/j.issn.1001-411X.202012006
|
To solve the problems of the machine-picked fresh tea leaves mixing with different grades of tea leaves, high mixing degree and low classification accuracy of physical characteristics.
Using the random forest classification model, a method based on the fusion of color and edge feature was proposed. We collected three different grades of fresh tea leaves, and processed the original images with cropping, size normalization and denoising, and then extracted the color features and edge features. Through parameter modification and testing, the optimal random forest classification model was constructed, and the comparison experiment was performed with the K-nearest neighbor and SVM classifier.
After feature fusion, the classification accuracy of random forest model reached 99.45%, which was 7.14 and 9.34 percentage points higher than those of single color feature and single edge feature, 15.38 and 5.49 percentage points higher than those of K-nearest neighbor model and SVM classifier respectively.
The established method can accurately separate single bud, one bud and one leaf, and one bud and two leaves of fresh tea leaves.
| [1] |
黄藩, 刘飞, 王云, 等. 计算机视觉技术在茶叶领域中的应用现状及展望[J]. 茶叶科学, 2019, 39(1): 81-87. doi: 10.3969/j.issn.1000-369X.2019.01.009
|
| [2] |
唐小林, 李文萃, 范起业. 机采茶鲜叶分类分级技术及相关设备研究进展[J]. 中国茶叶加工, 2015(2): 5-8.
|
| [3] |
陈全胜, 赵杰文, 蔡健荣, 等. 支持向量机在机器视觉识别茶叶中的应用研究[J]. 仪器仪表学报, 2006(12): 1704-1706. doi: 10.3321/j.issn:0254-3087.2006.12.032
|
| [4] |
吴正敏, 曹成茂, 王二锐, 等. 基于形态特征参数的茶叶精选方法[J]. 农业工程学报, 2019, 35(11): 315-321. doi: 10.11975/j.issn.1002-6819.2019.11.036
|
| [5] |
高震宇, 王安, 刘勇, 等. 基于卷积神经网络的鲜茶叶智能分选系统研究[J]. 农业机械学报, 2017, 48(7): 53-58. doi: 10.6041/j.issn.1000-1298.2017.07.007
|
| [6] |
余洪. 基于计算机视觉技术的茶叶品质分级研究[D]. 南昌: 江西农业大学, 2017.
|
| [7] |
金良海, 李德华. 基于噪声检测的图像去噪算法[J]. 模式识别与人工智能, 2008, 21(3): 298-302. doi: 10.3969/j.issn.1003-6059.2008.03.005
|
| [8] |
孙卫红, 黄志鹏, 梁曼, 等. 基于颜色特征和支持向量机的蚕茧分类方法研究[J]. 蚕业科学, 2020, 46(1): 86-95.
|
| [9] |
杨倩, 高晓阳, 武季玲, 等. 基于颜色和纹理特征的大麦主要病害识别研究[J]. 中国农业大学学报, 2013, 18(5): 129-135. doi: 10.11841/j.issn.1007-4333.2013.05.20
|
| [10] |
员伟康, 木拉提·哈米提, 严传波, 等. 基于颜色和纹理特征的新疆维吾尔医植物药材图像特征提取与判别分析[J]. 中国中医药信息杂志, 2016, 23(1): 78-81. doi: 10.3969/j.issn.1005-5304.2016.01.018
|
| [11] |
刘永波, 雷波, 胡亮, 等. 机器视觉在HSV颜色空间下稻瘟病病程分级判定研究[J]. 农学学报, 2020, 10(10): 83-90. doi: 10.11923/j.issn.2095-4050.cjas20190700108
|
| [12] |
苗荣慧, 杨华, 武锦龙, 等. 基于图像分块及重构的菠菜重叠叶片与杂草识别[J]. 农业工程学报, 2020, 36(4): 178-184. doi: 10.11975/j.issn.1002-6819.2020.04.021
|
| [13] |
谢文涌, 柴琴琴, 甘永辉, 等. 基于多特征提取和Stacking集成学习的金线莲品系分类[J]. 农业工程学报, 2020, 36(14): 203-210. doi: 10.11975/j.issn.1002-6819.2020.14.025
|
| [14] |
宋彦, 谢汉垒, 宁井铭, 等. 基于机器视觉形状特征参数的祁门红茶等级识别[J]. 农业工程学报, 2018, 34(23): 279-286. doi: 10.11975/j.issn.1002-6819.2018.23.036
|
| [15] |
段红燕, 邵豪, 张淑珍, 等. 一种基于Canny算子的图像边缘检测改进算法[J]. 上海交通大学学报, 2016, 50(12): 1861-1865.
|
| [16] |
BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
|
| [17] |
曹正凤. 随机森林算法优化研究[D]. 北京: 首都经济贸易大学, 2014.
|
| [18] |
陈进, 毛先成, 刘占坤, 等. 基于随机森林算法的大尹格庄金矿床三维成矿预测[J]. 大地构造与成矿学, 2020, 44(2): 231-241.
|
| [19] |
王猛, 张新长, 王家耀, 等. 结合随机森林面向对象的森林资源分类[J]. 测绘学报, 2020, 49(2): 235-244. doi: 10.11947/j.AGCS.2020.20190272
|
| [20] |
周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
|
| [21] |
王珏. 基于SVM和组合特征的分类算法研究[D]. 大连: 大连理工大学, 2017.
|
| [22] |
赵立新, 侯发东, 吕正超, 等. 基于迁移学习的棉花叶部病虫害图像识别[J]. 农业工程学报, 2020, 36(7): 184-191. doi: 10.11975/j.issn.1002-6819.2020.07.021
|
| 1. |
朱会军，吴嘉希，熊仁瑞，王雨滢，黄珏，董晓全，杨昊，罗贞，曾曙才，吴道铭. 基于Meta分析评价污泥施用对土壤团聚体的影响. 华南农业大学学报. 2025(01): 41-52 .
 本站查看
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: