A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

IF 3.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ioannis D. Apostolopoulos, Mpesi Tzani, Sokratis I. Aznaouridis
{"title":"A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features","authors":"Ioannis D. Apostolopoulos, Mpesi Tzani, Sokratis I. Aznaouridis","doi":"10.3390/ai4040041","DOIUrl":null,"url":null,"abstract":"Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This model leverages the learning capabilities of the recent successful networks for image classification called vision transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. The general model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with a 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%). However, it showed slightly lower performance in identifying guavas (97%), lemons (97%), limes (97.50%), mangoes (97.50%), pears (97%), and pomegranates (97%).","PeriodicalId":93633,"journal":{"name":"AI (Basel, Switzerland)","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI (Basel, Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/ai4040041","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This model leverages the learning capabilities of the recent successful networks for image classification called vision transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. The general model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with a 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%). However, it showed slightly lower performance in identifying guavas (97%), lemons (97%), limes (97.50%), mangoes (97.50%), pears (97%), and pomegranates (97%).
基于深度图像特征评估水果质量的通用机器学习模型
水果质量是农产品行业的关键因素,影响着生产者、分销商、消费者和经济。高品质的水果更有吸引力,营养更丰富,更安全,可以提高消费者的满意度和生产者的收入。人工智能可以通过图像来帮助评估水果的质量。本文提出了一种利用深度图像特征评估水果质量的通用机器学习模型。该模型利用了最近成功的图像分类网络的学习能力,称为视觉变压器(ViT)。结合各种水果数据集构建和训练ViT模型,并根据其视觉外观而不是预定义的质量属性来区分好水果和坏水果图像。通用模型在准确识别各种水果的质量方面表现出了令人印象深刻的结果,例如苹果(准确率为99.50%)、黄瓜(99%)、葡萄(100%)、kakis(99.50%)、橙子(99.50%)、木瓜(98%)、桃子(98%)、西红柿(99.50%)和西瓜(98%)。然而,它在识别番石榴(97%)、柠檬(97%)、酸橙(97.50%)、芒果(97.50%)、梨(97%)和石榴(97%)方面的表现稍低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.20
自引率
0.00%
发文量
0
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信