Efficient sweet potato quality detection using pruned attention-based deep learning

IF 2.7 2区 农林科学 Q1 ENTOMOLOGY
Huayu Fu , Guosheng Xin , Yanshen Zhao , Cong Wang , Zhongzhi Han
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引用次数: 0

Abstract

Sweet potatoes are rich in essential nutrients crucial for maintaining human health, and their market value is primarily determined by external quality. To address the classification of sweet potato appearance quality, we propose a rapid assessment method for evaluating external quality. Using a dataset of 1833 images of the Yanshu 25 variety, we adopt the MobileNetV4 series network with a reduced Universal Inverted Bottleneck (UIB) module as the core architecture. The network is trained and pruned by integrating a learning rate scheduler, a multi-head self-attention (MHSA) mechanism, and a pruning algorithm based on gated mechanisms. After pruning, the optimal model achieves an accuracy of 93.08 % across five categories, with an average detection speed of only 0.0022 s per sample. This method offers outstanding detection accuracy, faster processing speed, and significantly reduced model complexity, making it highly effective for sweet potato quality assessment.
基于修剪注意的深度学习的红薯质量检测
红薯富含对维持人体健康至关重要的必需营养素,其市场价值主要取决于外观质量。针对红薯外观质量的分类问题,提出了一种快速评价红薯外观质量的方法。以雁树25品种的1833张图像为数据集,采用基于简化的通用倒瓶颈(UIB)模块的MobileNetV4系列网络作为核心架构。通过集成学习率调度器、多头自关注(MHSA)机制和基于门控机制的修剪算法,对网络进行训练和修剪。经过修剪后,最优模型在5个类别中准确率达到93.08%,平均每个样本的检测速度仅为0.0022 s。该方法检测精度高,处理速度快,显著降低了模型复杂度,对红薯品质评价非常有效。
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来源期刊
CiteScore
5.70
自引率
18.50%
发文量
112
审稿时长
45 days
期刊介绍: The Journal of Stored Products Research provides an international medium for the publication of both reviews and original results from laboratory and field studies on the preservation and safety of stored products, notably food stocks, covering storage-related problems from the producer through the supply chain to the consumer. Stored products are characterised by having relatively low moisture content and include raw and semi-processed foods, animal feedstuffs, and a range of other durable items, including materials such as clothing or museum artefacts.
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