机器视觉在稻种识别和分类上的应用进展

doi:10.11924/j.issn.1000-6850.casb2024-0019

中国农学通报 ›› 2024, Vol. 40 ›› Issue (31): 152-158.doi: 10.11924/j.issn.1000-6850.casb2024-0019

机器视觉在稻种识别和分类上的应用进展

李边豪(), 张国良(), 李鹏程, 赵宏亮, 闫飞宇, 黄志炜, 牛远, 齐波, 张林青, 范松

淮阴工学院农学院/江淮平原作物产业工程研究院，江苏淮安 223003

收稿日期:2024-01-11 修回日期:2024-05-18 出版日期:2024-11-05 发布日期:2024-11-04
通讯作者:
张国良，男，1976年出生，江苏阜宁人，教授，博士，研究方向为作物智能化栽培。通信地址：223003 江苏省淮安市清江浦区淮阴工学院南苑淮阴工学院江淮平原作物产业工程研究院，Tel：0517-83591026，E-mail：hgzgl@126.com。
作者简介:
李边豪，男，2000年出生，江西上饶人，在读硕士，研究方向为作物图像识别与算法优化。通信地址：223003 江苏省淮安市清江浦区淮阴工学院南苑淮阴工学院江淮平原作物产业工程研究院，Tel：0517-83591026，E-mail：2674516482@qq.com。
基金资助:
全国农业重大技术协同推广项目“优良食味稻米产业重大技术协同推广计划”(2021-ZYXT-02-01); 淮安市乡村振兴项目“水稻基质棉固碳减排育秧技术集成创新与应用”(HAN202312)

Progress in Application of Machine Vision in Rice Seeds Recognition and Classification

LI Bianhao(), ZHANG Guoliang(), LI Pengcheng, ZHAO Hongliang, YAN Feiyu, HUANG Zhiwei, NIU Yuan, QI Bo, ZHANG Linqing, FAN Song

College of Agriculture, Huaiyin Institute of Technology/Jianghuai Plain Crop Industry Engineering Research Institute, Huai’an, Jiangsu 223003

Received:2024-01-11 Revised:2024-05-18 Published:2024-11-05 Online:2024-11-04

摘要/Abstract

摘要：

通过概述图像采集的2种方式，即RGB图像和光谱图像的获取，探讨图像预处理技术，包括图像去噪、增强及分割等步骤。在特征提取方面采用主成分分析(PCA)和线性判别分析(LDA)方法，能高效提取稻种的颜色、纹理和形状特征；探讨了机器学习和深度学习在稻种光谱图像及RGB图像处理中的实践应用，以及深度学习模型在稻种识别分类的性能优化和改进方法。研究发现，机器视觉技术在稻种识别领域展现出高效与准确性，未来可以期待开发出低成本的图像采集平台和更为轻量级的稻种识别软件，推动稻种数据共享，并持续探索新兴的深度学习技术，进一步优化稻种识别的效果。

关键词: 稻种分类, 稻种识别, 机器学习, 深度学习, 图像处理, 特征提取

Abstract:

The article first outlines two methods of image acquisition, namely, the capture of RGB images and spectral images. Subsequently, it explores image preprocessing techniques, including steps such as image denoising, enhancement, and segmentation. In terms of feature extraction, principal component analysis (PCA) and linear discriminant analysis (LDA) methods are employed to efficiently extract the color, texture, and shape features of rice seeds. Additionally, the article discusses the practical applications of machine learning and deep learning in processing spectral and RGB images of rice seeds, as well as the performance optimization and improvement methods of deep learning models in rice seed recognition and classification. Overall, machine vision technology demonstrates its efficiency and accuracy in the field of rice seed recognition. In the future, the development of a low-cost image acquisition platform and more lightweight rice seed recognition software can be anticipated, promoting rice seed data sharing and continuously exploring emerging deep learning techniques to further optimize the effectiveness of rice seed recognition.

Key words: classification of rice seeds, rice seed identification, machine learning, deep learning, image processing, feature extraction

李边豪, 张国良, 李鹏程, 赵宏亮, 闫飞宇, 黄志炜, 牛远, 齐波, 张林青, 范松. 机器视觉在稻种识别和分类上的应用进展[J]. 中国农学通报, 2024, 40(31): 152-158.

LI Bianhao, ZHANG Guoliang, LI Pengcheng, ZHAO Hongliang, YAN Feiyu, HUANG Zhiwei, NIU Yuan, QI Bo, ZHANG Linqing, FAN Song. Progress in Application of Machine Vision in Rice Seeds Recognition and Classification[J]. Chinese Agricultural Science Bulletin, 2024, 40(31): 152-158.

图/表 3

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编号	第一作者	发表年份	数据集/张	图片类型	网络框架	准确率/%
1	Temniranrat K	2020	3500	RGB	InceptionResNetV2	95.15
2	Koklu M	2021	75000	RGB	VGG16	100
3	Lin P	2018	5554	RGB	DCNN	92.1
4	Gilanie G	2021	1000	RGB	RiceNet	99
5	叶文超	2023	2500	光谱图像	2D-CNN	98
6	邱振军	2018	3000	光谱图像	CNN	90

编号	第一作者	发表年份	数据集/张	图片类型	网络框架	准确率/%
1	Temniranrat K	2020	3500	RGB	InceptionResNetV2	95.15
2	Koklu M	2021	75000	RGB	VGG16	100
3	Lin P	2018	5554	RGB	DCNN	92.1
4	Gilanie G	2021	1000	RGB	RiceNet	99
5	叶文超	2023	2500	光谱图像	2D-CNN	98
6	邱振军	2018	3000	光谱图像	CNN	90

机器视觉在稻种识别和分类上的应用进展

Progress in Application of Machine Vision in Rice Seeds Recognition and Classification

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