基于k均值聚类和CNN的芒果果实无创甜度计算机视觉评价

A. Yumang, Luvelin Anne G. Francia, Ryan Jowell L. Romero
{"title":"基于k均值聚类和CNN的芒果果实无创甜度计算机视觉评价","authors":"A. Yumang, Luvelin Anne G. Francia, Ryan Jowell L. Romero","doi":"10.1109/ICCAE56788.2023.10111250","DOIUrl":null,"url":null,"abstract":"Hailing from Guimaras, Philippines, the Carabao mango has recognition as the sweetest mango in the world. The Philippines should naturally be a top global mango exporter, for that matter. The distribution system and workforce of the country, however, are lacking. Marketing and labeling yellow, ripe Carabao mangoes as sweet when some are sour easily mislead the human eye. The automated non-invasive sorting of ripe Carabao mangoes as Super Sweet, Sweet, or Sour relative to their yellow hue, Brix value, and the range the mangoes belong under can create leverage for the Philippine mango distribution. Sixty images garnered from two (2) sides of 30 ripe Carabao mango test samples went first through Convolutional Neural Network (CNN) to segment the mango from other unnecessary fragments. The grouped most dominant colors of K-means clustering then produce RGB values in Carabao mangoes. Those RGB values correspond to Brix values, and the higher the Brix values, the sweeter the mango. Classifications of the computer vision system achieved 83.33% accuracy and 16.67% misclassification.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Computer Vision-Based Non-invasive Sweetness Assessment of Mangifera Indica L. Fruit Using K-means Clustering and CNN\",\"authors\":\"A. Yumang, Luvelin Anne G. Francia, Ryan Jowell L. Romero\",\"doi\":\"10.1109/ICCAE56788.2023.10111250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hailing from Guimaras, Philippines, the Carabao mango has recognition as the sweetest mango in the world. The Philippines should naturally be a top global mango exporter, for that matter. The distribution system and workforce of the country, however, are lacking. Marketing and labeling yellow, ripe Carabao mangoes as sweet when some are sour easily mislead the human eye. The automated non-invasive sorting of ripe Carabao mangoes as Super Sweet, Sweet, or Sour relative to their yellow hue, Brix value, and the range the mangoes belong under can create leverage for the Philippine mango distribution. Sixty images garnered from two (2) sides of 30 ripe Carabao mango test samples went first through Convolutional Neural Network (CNN) to segment the mango from other unnecessary fragments. The grouped most dominant colors of K-means clustering then produce RGB values in Carabao mangoes. Those RGB values correspond to Brix values, and the higher the Brix values, the sweeter the mango. Classifications of the computer vision system achieved 83.33% accuracy and 16.67% misclassification.\",\"PeriodicalId\":406112,\"journal\":{\"name\":\"2023 15th International Conference on Computer and Automation Engineering (ICCAE)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 15th International Conference on Computer and Automation Engineering (ICCAE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAE56788.2023.10111250\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAE56788.2023.10111250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

卡拉宝芒果来自菲律宾吉马拉斯,被认为是世界上最甜的芒果。就此而言,菲律宾自然应该是全球最大的芒果出口国。然而,该国缺乏分配系统和劳动力。销售和标签黄色,成熟的卡拉宝芒果是甜的,而有些是酸的很容易误导人眼。成熟的卡拉宝芒果根据其黄色色调、糖度值和芒果所属的范围,按照超甜、甜或酸进行自动无创分类,可以为菲律宾芒果的分销创造有利条件。从30个成熟的卡拉巴芒果测试样本的两面收集的60张图像首先通过卷积神经网络(CNN)将芒果从其他不必要的碎片中分割出来。然后,K-means聚类的最主色分组产生卡拉宝芒果的RGB值。这些RGB值对应于糖度值,糖度值越高,芒果越甜。计算机视觉分类准确率为83.33%,误分类率为16.67%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computer Vision-Based Non-invasive Sweetness Assessment of Mangifera Indica L. Fruit Using K-means Clustering and CNN
Hailing from Guimaras, Philippines, the Carabao mango has recognition as the sweetest mango in the world. The Philippines should naturally be a top global mango exporter, for that matter. The distribution system and workforce of the country, however, are lacking. Marketing and labeling yellow, ripe Carabao mangoes as sweet when some are sour easily mislead the human eye. The automated non-invasive sorting of ripe Carabao mangoes as Super Sweet, Sweet, or Sour relative to their yellow hue, Brix value, and the range the mangoes belong under can create leverage for the Philippine mango distribution. Sixty images garnered from two (2) sides of 30 ripe Carabao mango test samples went first through Convolutional Neural Network (CNN) to segment the mango from other unnecessary fragments. The grouped most dominant colors of K-means clustering then produce RGB values in Carabao mangoes. Those RGB values correspond to Brix values, and the higher the Brix values, the sweeter the mango. Classifications of the computer vision system achieved 83.33% accuracy and 16.67% misclassification.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信