Jiarui Cui, Shubin Cao, Jie Hao, Yao Zhang, Shuang Gao, Jianshe Li, Fatimah Shuang Ma, Longguo Wu
{"title":"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","authors":"Jiarui Cui, Shubin Cao, Jie Hao, Yao Zhang, Shuang Gao, Jianshe Li, Fatimah Shuang Ma, Longguo Wu","doi":"10.1016/j.indcrop.2025.121909","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13581,"journal":{"name":"Industrial Crops and Products","volume":"53 1","pages":""},"PeriodicalIF":6.2000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Construction of an industrial detection model for tomato organic acids based on low-rank adaptation and spatial feature fusion\",\"authors\":\"Jiarui Cui, Shubin Cao, Jie Hao, Yao Zhang, Shuang Gao, Jianshe Li, Fatimah Shuang Ma, Longguo Wu\",\"doi\":\"10.1016/j.indcrop.2025.121909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13581,\"journal\":{\"name\":\"Industrial Crops and Products\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Industrial Crops and Products\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1016/j.indcrop.2025.121909\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Crops and Products","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1016/j.indcrop.2025.121909","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
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.
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.