ArticleSensors (Basel, Switzerland)2020
Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- FDA-YOLO: A Feature Fusion and Attention-Based Network for Multiscale Tomato Maturity Detection in Real-World Agricultural Scenarios.Sensors (Basel, Switzerland) · 2026Article
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review.Frontiers in plant science · 2023Review
- Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity.Sensors (Basel, Switzerland) · 2022Article
- Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review.Foods (Basel, Switzerland) · 2022Review
- A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550-1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers.
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Registered trials
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