ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1690652
This article is part of the Research TopicSmart Sensing in Plant Science: Advancing Plant-Environment Interactions for Sustainable PhytoprotectionView all 3 articles
Research on Non-destructive Detection Model of Tomato Fruit Quality Based on Electrical Properties and Machine Learning Algorithms
Provisionally accepted
- 1Tarim University, Aral, China
- 2Institute of Agricultural Sciences, alar, China
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To achieve non-destructive detection of tomato internal fruit quality, this study proposes a novel predictive method that integrates a Long Short-Term Memory Autoencoder (LSTMAE) and XGBoost (LSTMAE–XGBoost). This method combines the feature extraction capabilities of the autoencoder, the sequence data processing abilities of LSTM, and the high-precision prediction capabilities of XGBoost. Within the frequency range of 0.1–1000 kHz, electrical parameters such as parallel equivalent capacitance, parallel equivalent resistance, and quality factor—among nine electrical parameters—were obtained from 300 tomato samples using an electrical parameter analyzer. Additionally, four indicators—vitamin C, soluble sugar, soluble protein, and titratable acidity—were obtained through physicochemical analysis of the tomatoes. Based on the electrical parameters and internal physicochemical indicator data of the tomatoes, a non-destructive detection model for tomato internal quality indicators was constructed. Experimental results demonstrate that the LSTMAE– XGBoost model exhibits superior adaptability. In the test set, the coefficients of determination for vitamin C, soluble sugar, soluble protein, and titratable acidity were 0.805, 0.945, 0.838, and 0.845, respectively. Compared to traditional machine learning models, this model offers better prediction accuracy. It improves upon the traditional Pearson correlation coefficient (PCC) feature extraction method by 14.3%, 13.1%, 7.8%, and 9.5%, respectively. Furthermore, LSTMAE–XGBoost can simultaneously predict all four indicators, enhancing the model's efficiency. Therefore, LSTMAE– XGBoost can be utilized as an effective ensemble model for non-destructive detection of tomato internal quality indicators, which holds significant importance for fruit quality non-destructive detection in the horticultural industry
Keywords: tomatoes, Electrical parameters, fruit quality, LSTMAE-XGBoost, Non-destructive detection
Received: 22 Aug 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 婷婷, Zhanming, Cheng, Ma and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tan Zhanming, tlmdxtzm@taru.edu.cn
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