一种基于偏好免疫网络多特征辨识的油茶果分选识别方法

doi:10.13733/j.jcam.issn.2095-5553.2021.09.26

摘要/Abstract

摘要： 针对油茶果采摘、脱壳后机器视觉分选效率不高的问题,提出一种多特征偏好人工免疫网络算法,该算法应用人工免疫网络的多目标优化与偏好数据库特征,提取油茶目标的颜色、形态多特征输入免疫网络进行仿真测试。试验结果表明,本文提出的多特征偏好免疫网络的识别率最高达到90%以上。相比单特征分选方法有了较大的提升,证明本文分选方法的有效性,并为农林业目标智能化分选辨识提供一种可行的方案。

关键词: 分选, 机器视觉, 油茶果, 人工免疫网络, 多特征

Abstract: In order to solve the problem of low sorting efficiency after picking and shelling of Camellia fruits in machinevision, the paper proposes a preference artificial immune network algorithm (aiNet) with multifeatures, which applies the multiobjective optimization of artificial immune network and preference database to extract the multifeatures in color and shape of Camellia objects to input into the immune network for simulation test. The test results show that the multifeatures preference immune network proposed in this paper is feasible. The recognition rate of the network has reached 90%, and the minimum identification time is 60 ms. Compared with the single feature sorting method, this method is more effective, which provides a feasible scheme for the intelligent sorting method of agricultural and forestry targets.

Key words: sorting, machinevision, Camellia fruit, aiNet, multifeatures

中图分类号:

李昕, 陈泽君, 李立君, 谭季秋, 吴发展, . 一种基于偏好免疫网络多特征辨识的油茶果分选识别方法[J]. 中国农机化学报, 2021, 42(9): 187-194.

Li Xin, Chen Zejun, Li Lijun, Tan Jiqiu, Wu Fazhan. . A method of Camellia fruit sorting based on multifeatures identification in preference aiNet[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(9): 187-194.

参考文献

［１］　
康地, 陈泽君, 李阳, 等. 油茶果分级滚筒筛设计［J］. 湖南林业科技, 2017, 44(6): 95-98.

Kang Di, Chen Zejun, Li Yang, et al. Design of classificationtype roller sieve for Camellia oleifera fruit ［J］. Hunan Forestry Science & Technology, 2017, 44(6): 95-98.

［2］　
李阳, 王勇, 邓腊云, 等. 揉搓型油茶果分类脱壳分选机的脱壳和清选效果研究［J］. 湖南林业科技, 2015, 42(2): 38-42.

Li Yang, Wang Yong, Deng Layun, et al. Research on the effect of Camellia oleifera fruit sheller and sorting machine, 2015, 42(2): 38-42.

［3］　
Thendral R, Suhasini A, Senthil N. A comparative analysis of edge and color based segmentation for orange fruit recognition ［C］. International Conference on Communications & Signal Processing. IEEE, 2014.

［4］　
Lin G, Tang Y, Zou X, et al. Fruit detection in natural environment using partial shape matching and probabilistic Hough transform ［J］. Precision Agriculture, 2019, 21(1): 160-177.

［5］　
苗中华, 沈一筹, 王小华, 等. 自然环境下重叠果实图像识别算法与试验［J］. 农业机械学报, 2016, 47(6): 21-26.

Miao Zhonghua, Shen Yichou, Wang Xiaohua, et al. Image recognition algorithm and experiment of overlapped fruits in natural environment ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(6): 21-26.

［6］　
Wang Jianlun, He Jianlei, Han Yu, et al. An adaptive thresholding algorithm of field leaf image ［J］. Computers and Electronics in Agriculture, 2013, 96(6): 23-39.

［7］　
Pothen Z, Nuske S. Automated assessment and mapping of grape quality through imagebased color analysis ［J］. IFACPapersOnLine, 2016, 49(16): 72-78.

［8］　
MurilloBracamontes E A, MartinezRosas M E, MirandaVelasco M M, et al. Implementation of Hough transform for fruit image segmentation ［J］. Procedia Engineering, 2012, 35: 230-239.

［9］　
周志宇, 刘迎春, 张建新. 基于自适应Canny算子的柑橘边缘检测［J］. 农业工程学报, 2008, 24(3): 21-24.

Zhou Zhiyu, Liu Yingchun, Zhang Jianxin. Orange edge detection based on adaptive Canny operator ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(3): 21-24.

［10］　
刘智杭, 于鸣, 任洪娥. 基于改进K均值聚类的葡萄果穗图像分割［J］. 江苏农业科学, 2018, 46(24): 239-244.

［11］　
武锦龙, 苗荣慧, 黄锋华, 等. 高光谱图像与卷积神经网络相结合的油桃轻微损伤检测［J］. 山西农业大学学报(自然科学版), 2019, 39(2): 79-85.

Wu Jinlong, Miao Ronghui, Huang Fenghua, et al. Nectarine slight bruises detection based on the combination of hyperspectral image and convolutional neural network ［J］. Journal of Shanxi Agricultural University (Natural Science Edition), 2019, 39(2): 79-85.

［12］　
李凯, 张建华, 冯全, 等. 复杂背景与天气条件下的棉花叶片图像分割方法［J］. 中国农业大学学报, 2018, 23(2): 88-98.

Li Kai, Zhang Jianhua, Feng Quan, et al. Image segmentation method for cotton leaf under complex background and weather conditions ［J］. Journal of China Agricultural University, 2018, 23(2): 88-98.

［13］　
邓向武, 齐龙, 马旭, 等. 基于多特征融合和深度置信网络的稻田苗期杂草识别［J］. 农业工程学报, 2018, 34(14): 165-172.

Deng Xiangwu, Qi Long, Ma Xu, et al. Recognition of weeds at seedling stage in paddy fields using multifeature fusion and deep belief networks ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(14): 165-172.

［14］　
Wang Qi, Wang Hui, Xie Lijuan, et al. Outdoor color rating of sweet cherries using computer vision ［J］. Computers and Electronics in Agriculture, 2012, 87: 113-120.

［15］　
Pearson T, Dan M, Pearson J. A machine vision system for high speed sorting of small spots on grains ［J］. Journal of Food Measurement & Characterization, 2012, 6(1-4): 27-34.

［16］　
赵吉文, 魏正翠, 汪洋, 等. 基于灰度带比例的优质西瓜子识别算法研究与实现［J］. 农业工程学报, 2011, 27(4): 340-344.

Zhao Jiwen, Wei Zhengcui, Wang Yang, et al. Research and implementation of recognition algorithm based on gray scale of watermelon seeds ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(4): 340-344.

［17］　
王丹丹, 何东健. 基于RFCN深度卷积神经网络的机器人疏果前苹果目标的识别［J］. 农业工程学报, 2019, 35(3): 156-163.

Wang Dandan, He Dongjian. Recognition of apple targets before fruits thinning by robot based on RFCN deep convolution neural network ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(3): 156-163.

［18］　
Masuda H, Nojima Y, Ishibuchi H. Visual examination of the behavior of EMO algorithms for manyobjective optimization with many decision variables ［C］. Evolutionary Computation. IEEE, 2014: 2633-2640.

［19］　
崔鹏宇. 多维特征融合与AdaboostSVM的车辆识别算法［J］. 控制工程, 2019, 26(3): 608-612.

Cui Pengyu. A vehicle target distinguish algorithm based on multifeature fusion and AdaboostSVM ［J］. Control Engineering of China, 2019, 26(3): 608-612.

[1]	王珊, 薛新宇, 郭祥雨. 基于机器视觉的油菜菌核病分级检测研究[J]. 中国农机化学报, 2022, 43(5): 47-53.
[2]	刘媛杰, 张泽亮, 张洪洲, 李勇, 李伟强. 基于机器视觉技术的棉籽破损检测[J]. 中国农机化学报, 2022, 43(5): 71-76.
[3]	郑如新, 孙青云, 肖国栋. 基于机器视觉的金银花图像识别处理算法研究[J]. 中国农机化学报, 2022, 43(4): 153-159.
[4]	冯凯, 王茂励, 何晓宁, 王东伟, 岳丹松, 初香港. 基于辅助线的农机视觉导航路径提取算法研究[J]. 中国农机化学报, 2022, 43(10): 167-175.
[5]	伍德林, 杨俊华, 刘芸, 赵恩龙, 刘路, 曹成茂. 我国油茶果采摘装备研究进展与趋势[J]. 中国农机化学报, 2022, 43(1): 186-194.
[6]	白云龙;傅彬;史振华;王健;. 基于标记分水岭算法的插秧机器人导航路径检测[J]. 中国农机化学报, 2021, 42(7): 142-147.
[7]	刘海涛;伊丽丽;兰玉彬;韩鑫;崔立华;. 机器视觉在棉花智能打顶领域的应用研究进展[J]. 中国农机化学报, 2021, 42(6): 159-165.
[8]	吕沐华;丁珠玉;. 基于机器视觉的果园喷药除草机器人视觉系统设计[J]. 中国农机化学报, 2021, 42(5): 42-48.
[9]	郭祥雨;薛新宇;. 基于机器视觉的水稻制种田导航线提取方法[J]. 中国农机化学报, 2021, 42(5): 197-201.
[10]	吕梦棋;张芮祥;贾浩;马丽;. 基于改进ResNet玉米种子分类方法研究[J]. 中国农机化学报, 2021, 42(4): 92-98.
[11]	杨涛;李晓晓;. 机器视觉技术在现代农业生产中的研究进展[J]. 中国农机化学报, 2021, 42(3): 171-181.
[12]	谢仕烁;黄伟锋;朱立学;杨尘宇;张世昂;付根平;. 基于改进Floodfill算法的田垄视觉导航系统[J]. 中国农机化学报, 2021, 42(3): 182-188.
[13]	张亚军;. 基于改进支持向量机算法的农业害虫图像识别研究[J]. 中国农机化学报, 2021, 42(2): 146-152.
[14]	金耀;何秀文;万世主;刘仁鑫;. 基于YOLO v3的生猪个体识别方法[J]. 中国农机化学报, 2021, 42(2): 178-183.
[15]	张文蓉, 王朋, 钟鸣, 黄博, 曹景军, 柴秀娟, . 基于视觉的茭白自动分级包装设备研究与开发[J]. 中国农机化学报, 2021, 42(12): 114-121.