Detection of water content and size of peas based on hyperspectral imaging combined with 2D-CNN and irregular polygon size measurement techniques

IF 4.6 2区 农林科学 Q2 CHEMISTRY, APPLIED
You-fei Hou , Yan-bing Li , Nan Chen , Shang-tao Ou-yang , Qi Wang , Yang Wang , Bin Li , Yan-de Liu
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引用次数: 0

Abstract

Pea storage stability and germination rely on moisture content and morphology, but traditional destructive methods cause sample damage, low efficiency, and subjective errors, limiting practical use. To overcome the destructive and inefficient limitations of traditional methods for pea quality assessment, this study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning. We innovatively converted one-dimensional spectral data into two-dimensional texture images via Gramian Angular Field (GAF) encoding and input them into a residual 2D Convolutional Neural Network (2D-CNN) for moisture prediction. For dimensional analysis, a novel algorithm based on irregular polygon geometry was proposed to accurately measure pea length and width. The GAF-2D-CNN model achieved superior performance for moisture prediction (prediction set R²=0.9818, RMSEP=0.0318 %, RPD=7.4780), significantly outperforming 1D-CNN, Least Squares Support Vector Machine (LSSVM), and Partial Least Squares Regression (PLSR) models. The dimensional algorithm also demonstrated high accuracy, especially for length measurement (R²=0.9946, RPD=13.94). This framework provides a robust, accurate, and high-throughput solution for automated pea quality grading, offering significant potential for applications in precision agriculture and storage management.
基于2D-CNN和不规则多边形尺寸测量技术的高光谱成像豌豆含水量和尺寸检测
豌豆的储存稳定性和发芽依赖于水分含量和形态,但传统的破坏性方法会造成样品损坏、效率低、主观误差,限制了实际应用。为了克服传统豌豆质量评估方法破坏性和低效的局限性,本研究开发了一个集成的、无损的框架,用于同时快速测量豌豆水分含量和大小,使用高光谱成像结合深度学习。我们创新地将一维光谱数据通过格拉曼角场(GAF)编码转换为二维纹理图像,并将其输入残差二维卷积神经网络(2D- cnn)进行湿度预测。在尺寸分析方面,提出了一种基于不规则多边形几何的豌豆长、宽精确测量算法。GAF-2D-CNN模型在水分预测方面取得了较好的效果(预测集R²=0.9818,RMSEP=0.0318 %,RPD=7.4780),显著优于1D-CNN、最小二乘支持向量机(LSSVM)和偏最小二乘回归(PLSR)模型。尺寸算法也具有较高的精度,特别是长度测量(R²=0.9946,RPD=13.94)。该框架为自动化豌豆质量分级提供了一个强大、准确和高通量的解决方案,在精准农业和存储管理方面具有巨大的应用潜力。
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来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
期刊介绍: The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.
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