对高光谱图像管道进行评估,以建立具有通用能力的作物干物质含量预测模型

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Ioannis Malounas, Borja Espejo-Garcia, Konstantinos Arvanitis, Spyros Fountas
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引用次数: 0

摘要

高光谱成像技术已被证明是估算干物质的可靠技术,而干物质是考虑新鲜农产品质量时的一个常见变量。然而,开发能够适用于不同作物的模型具有挑战性。在这项研究中,对实现稳健、准确的通用回归模型的几种管道进行了评估,并开发了用于水果和蔬菜干物质估算的自动相关性确定(ARD)和偏最小二乘法(PLS)算法。这些模型是使用 VIS-NIR 数据集建立的,其中包括水果和蔬菜,即苹果、西兰花和韭菜(n = 779)。在 10x5 倍交叉验证协议中,PLS 回归模型的预测均方根(RMSEP)= 0.0137,优于 ARD 回归(RMSEP = 0.0140)。所评估的预处理技术对两种回归算法的影响各不相同,在不进行特征提取的情况下,管道的效果最好。总体而言,使用 ARD 或 PLS 回归的管道在基于可见光-近红外(VIS-NIR)的各种水果和蔬菜干物质估算中表现出很强的性能和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model

Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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