Detection of early bruises in apples using hyperspectral imaging and an improved MobileViT network

瘀伤 高光谱成像 人工智能 计算机科学 稳健性(进化) 模式识别(心理学) 计算机视觉 医学 放射科 生物化学 基因 化学
作者
Mianqing Yang,Guoliang Chen,Feng Lv,Yunyun Ma,Yiyun Wang,Qingdian Zhao,Dayang Liu
出处
期刊:Journal of Food Science [Wiley]
标识
DOI:10.1111/1750-3841.17512
摘要

Abstract Apples are susceptible to postharvest bruises, leading to a shortened shelf life and significant waste. Therefore, accurate detection of apple bruises is crucial to mitigate food waste. This study proposed an improved lightweight network based on MobileViT for detecting early‐stage bruises in apples, utilizing hyperspectral imaging technology from 397.66 to 1003.81 nm. After acquiring hyperspectral images, the Otsu threshold algorithm was employed for mask extraction, and principal component analysis was used for feature image extraction. Subsequently, the improved MobileViT network (iM‐ViT) was implemented and compared with traditional algorithms, utilizing depthwise separable convolutions for parameter reduction and integrating local and global features to enhance bruise detection capability. The results demonstrated the superior performance of iM‐ViT in accurately detecting apple bruises, showing significant improvements. The F 1 score and test accuracy for detecting apple bruises using iM‐ViT reached 0.99 and 99.07%, respectively. The fivefold cross‐validation strategy was used to assess the stability and robustness of iM‐ViT, and ablation experiments were performed to explore the effects of depthwise separable convolutions and local features on parameter reduction and classification accuracy improvement for early‐stage bruise detection in apples. The results demonstrated that iM‐ViT effectively reduced parameters and improved the ability to detect early bruises in apples. Practical Application This study proposed an improved lightweight network to detect early bruises in apples, providing a reference for quick detection of bruises caused in the production process. Potential insights into the nondestructive detection of apple bruises using lightweight networks have been presented, which might be applied to mobile or online devices.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
tough_cookie完成签到 ,获得积分10
1秒前
彩钢房完成签到,获得积分10
2秒前
MeSs完成签到 ,获得积分10
3秒前
toxikon完成签到,获得积分10
4秒前
一点通完成签到,获得积分10
4秒前
Lei完成签到,获得积分10
5秒前
5秒前
5秒前
常若冰完成签到,获得积分10
5秒前
纯真的元风完成签到,获得积分10
6秒前
哇哈哈哈完成签到,获得积分10
6秒前
清秋1001完成签到 ,获得积分10
7秒前
qq完成签到,获得积分10
8秒前
荒野风发布了新的文献求助10
9秒前
Zxx发布了新的文献求助10
10秒前
11秒前
11秒前
确幸完成签到 ,获得积分10
11秒前
苒苒完成签到,获得积分10
11秒前
12秒前
酷波er应助c123采纳,获得10
12秒前
TIAOTIAO完成签到,获得积分10
14秒前
未晚完成签到 ,获得积分10
14秒前
15秒前
15秒前
天天快乐应助qinglinglie采纳,获得10
15秒前
自由老头应助荒野风采纳,获得10
15秒前
本末倒纸发布了新的文献求助10
16秒前
16秒前
甜蜜老虎完成签到,获得积分10
16秒前
脑洞疼应助帅气的蚊子采纳,获得10
17秒前
你好完成签到 ,获得积分10
18秒前
19秒前
19秒前
XZZ完成签到 ,获得积分10
19秒前
20秒前
21秒前
小阿飞完成签到,获得积分10
22秒前
秀丽的初柔完成签到,获得积分10
22秒前
量子星尘发布了新的文献求助10
23秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038619
求助须知:如何正确求助?哪些是违规求助? 3576294
关于积分的说明 11375058
捐赠科研通 3306084
什么是DOI,文献DOI怎么找? 1819374
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066