Yang Mianqing, Chen Guoliang, Lv Feng, Ma Yunyun, Wang Yiyun, Zhao Qingdian, Liu Dayang
College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
J Food Sci. 2024 Dec;89(12):8581-8593. doi: 10.1111/1750-3841.17512. Epub 2024 Nov 4.
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 F1 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.
苹果易受采后擦伤影响，导致货架期缩短和大量浪费。因此，准确检测苹果擦伤对于减少食物浪费至关重要。本研究提出了一种基于MobileViT的改进型轻量级网络，用于检测苹果早期擦伤，利用397.66至1003.81纳米的高光谱成像技术。获取高光谱图像后，采用大津阈值算法进行掩膜提取，并使用主成分分析进行特征图像提取。随后，实现了改进的MobileViT网络（iM-ViT）并与传统算法进行比较，利用深度可分离卷积减少参数，并整合局部和全局特征以增强擦伤检测能力。结果表明iM-ViT在准确检测苹果擦伤方面具有卓越性能，有显著提升。使用iM-ViT检测苹果擦伤的F1分数和测试准确率分别达到0.99和99.07%。采用五折交叉验证策略评估iM-ViT的稳定性和鲁棒性，并进行消融实验，以探究深度可分离卷积和局部特征对减少参数和提高苹果早期擦伤检测分类准确率的影响。结果表明iM-ViT有效减少了参数，并提高了检测苹果早期擦伤的能力。实际应用：本研究提出了一种改进的轻量级网络来检测苹果早期擦伤，为快速检测生产过程中产生的擦伤提供了参考。提出了使用轻量级网络对苹果擦伤进行无损检测的潜在见解，这些见解可能应用于移动或在线设备。
