Inversion of the Optical Properties of Apples Based on the Convolutional Neural Network and Transfer Learning Methods

Author:

Li Yibai,Li Yibai,Wang Haoyun,Zhang Yuzhuo,Wang Jiangbo,Xu Huanliang,Wang Haoyun,Zhang Yuzhuo,Wang Jiangbo,Xu Huanliang

Abstract

Highlights Convolutional neural network and MMD transfer learning methods are applied in inversion of optical properties. The classification accuracy of apples’ peel and pulp absorption coefficients are 84.61% and 92.47%, the accuracy of peel and pulp scattering coefficients are 83.56% and 86.53%, respectively. The depth optical characteristics can better reflect brix and moisture of apple then optical properties and hyperspectral data, the correlations are in the form of 0.98 and 0.98. Abstract. An inversion of optical properties is an important test for determining the quality of fruit. The conventional inversion model of the optical properties uses measured hyperspectral images as the training data. Studies show that the conventional machine learning method for inverting the optical properties results in low inversion accuracy, especially with curved models. Hence, the present study uses a convolutional neural network scheme to train the simulated hyperspectral images. Moreover, the maximum mean discrepancy (MMD) transfer method is used to transfer the simulated hyperspectral images to the measured hyperspectral images of apples. To evaluate the performance of the proposed method, the present study uses it to classify a variety of an apple’s optical properties, including the peel absorption, pulp absorption, peel scattering, and pulp scattering coefficients. The classification accuracies of the peel and pulp absorption coefficients are 84.61% and 92.47%, respectively. The classification accuracies of the peel and pulp scattering coefficients are 83.56% and 86.53%, respectively. These inversion results are compared with convolutional neural networks, neural networks, and support vector machines with measured hyperspectral images. It was found that the proposed inversion model is an effective scheme for optical property inversion. To prove the necessity of optical property inversion, the least squares, decision tree and random forest regression methods are performed to analyze the correlation between the depth of optical characteristics and the brix and moisture. The present study shows that these correlations are in the form of 0.98 and 0.98. The correlation coefficients increase by 0.36 and 0.25 compared to the measured hyperspectral images. The conclusions show that the proposed inversion model is an effective scheme for apple optical property inversion. Keywords: Apple tissue, Hyperspectral, Optical property inversion, Quality inspection.

Publisher

American Society of Agricultural and Biological Engineers (ASABE)

Subject

General Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3