利用深度学习和评分机制在西瓜籽分类中应用高光谱成像技术

IF 2.6 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Hengnian Qi, Mengbo He, Zihong Huang, Jianfang Yan, Chu Zhang
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引用次数: 0

摘要

西瓜籽是饮食中重要的营养来源。为了评估近红外高光谱成像技术在快速、无损地识别西瓜籽品种方面的潜力,我们采用了近红外高光谱成像(NIR-HSI)技术。萨维茨基-戈莱(SG)平滑算法和标准正态变量(SNV)算法相结合,对提取的光谱数据进行预处理。连续投影算法(SPA)用于降低光谱数据的维度。随后,使用三种深度学习模型(LeNet、GoogLeNet 和 ResNet)对 10 种常见的西瓜籽进行分类。SPA 用于降低高光谱数据的维度。就全波段而言,ResNet 模型在测试集上的分类准确率达到了 86.77%。通过使用特征波段,GoogLeNet 模型在测试集上的分类准确率达到了 83.85%。基于评分机制的集合融合模型在训练集、验证集和测试集上的准确率分别达到了 99.56%、90.88% 和 87.97%。结果表明,基于评分机制的集合融合模型可以提高准确率。将深度学习与近红外-红外成像技术相结合,可以有效区分不同品种的西瓜种子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of Hyperspectral Imaging for Watermelon Seed Classification Using Deep Learning and Scoring Mechanism

Application of Hyperspectral Imaging for Watermelon Seed Classification Using Deep Learning and Scoring Mechanism

Watermelon seeds are a significant source of nutrition in the diet. To assess the potential of near-infrared hyperspectral imaging technology for swift and nondestructive identification of watermelon seed varieties, near-infrared hyperspectral imaging (NIR-HSI) technology was used. The Savitzky–Golay (SG) smoothing algorithm and standard normal variable (SNV) algorithm were combined to preprocess the extracted spectral data. The successive projections algorithm (SPA) was used to reduce the dimensionality of the spectral data. Subsequently, three deep learning models (LeNet, GoogLeNet, and ResNet) were used to classify 10 common watermelon seeds. SPA was used to reduce the dimensionality of hyperspectral data. In terms of full band, the ResNet model achieved a classification accuracy of 86.77% on the test set. By using characteristic bands, the GoogLeNet model achieved a classification accuracy of 83.85% on the test set. The ensemble fusion model based on a scoring mechanism achieved accuracy rates of 99.56%, 90.88%, and 87.97% on the training, validation, and test sets, respectively. The results indicated that the ensemble fusion model based on a scoring mechanism can enhance accuracy. Combining deep learning with NIR-HSI can effectively distinguish different varieties of watermelon seeds.

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来源期刊
Journal of Food Quality
Journal of Food Quality 工程技术-食品科技
CiteScore
5.90
自引率
6.10%
发文量
285
审稿时长
>36 weeks
期刊介绍: Journal of Food Quality is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles related to all aspects of food quality characteristics acceptable to consumers. The journal aims to provide a valuable resource for food scientists, nutritionists, food producers, the public health sector, and governmental and non-governmental agencies with an interest in food quality.
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