基于低阶自适应和空间特征融合的番茄有机酸工业检测模型构建

IF 6.2 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Jiarui Cui, Shubin Cao, Jie Hao, Yao Zhang, Shuang Gao, Jianshe Li, Fatimah Shuang Ma, Longguo Wu
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引用次数: 0

摘要

番茄(Solanum lycopersicum L.)不仅是全球重要的人类消费作物,而且是具有工业价值的有机酸的潜在来源。柠檬酸、苹果酸等有机酸在生物基化学合成、发酵、生物降解塑料、绿色溶剂等方面有着广泛的应用。在这项研究中,我们开发了一种新的非破坏性框架,将低秩自适应(LoRA)与自适应空间特征融合网络(ASFFN)相结合,利用高光谱成像(HSI)准确预测有机酸含量。该模型集成了先进的特征融合策略和鲁棒异常值检测,以提高番茄品种的泛化性。对比实验表明,所提出的LoRA-ASFFN优于传统的CNN和PLSR基线。该模型能够快速准确地识别高有机酸浓度的西红柿,为工业加工和提取提供了有效的途径。该研究通过数据驱动的精确筛选技术,有助于推进生物基化学品供应链,提高番茄作物的经济价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of an industrial detection model for tomato organic acids based on low-rank adaptation and spatial feature fusion
Tomato (Solanum lycopersicum L.) is not only a globally important crop for human consumption but also a potential source of industrially valuable organic acids. Organic acids such as citric and malic acid have widespread applications in bio-based chemical synthesis, fermentation, biodegradable plastics, and green solvents. In this study, we develop a novel non-destructive framework combining Low-Rank Adaptation (LoRA) with Adaptive Spatial Feature Fusion Network (ASFFN) to accurately predict organic acid content using hyperspectral imaging (HSI). The model integrates advanced feature fusion strategies and robust outlier detection to improve generalizability across tomato varieties. Comparative experiments demonstrate the superior performance of the proposed LoRA-ASFFN over conventional CNN and PLSR baselines. The model enables rapid and precise identification of tomatoes with high organic acid concentrations, providing an efficient pathway for industrial processing and extraction. This research contributes to advancing bio-based chemical supply chains and improving the economic value of tomato crops through data-driven, precision screening techniques.
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来源期刊
Industrial Crops and Products
Industrial Crops and Products 农林科学-农业工程
CiteScore
9.50
自引率
8.50%
发文量
1518
审稿时长
43 days
期刊介绍: Industrial Crops and Products is an International Journal publishing academic and industrial research on industrial (defined as non-food/non-feed) crops and products. Papers concern both crop-oriented and bio-based materials from crops-oriented research, and should be of interest to an international audience, hypothesis driven, and where comparisons are made statistics performed.
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