Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy

Huang, Yuping; Si, Wan; Chen, Kunjie; Sun, Yi

doi:10.3390/s20247229

Cited by 21 publications

(13 citation statements)

References 31 publications

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“…In recent years, SRS has been widely used in the field of food analysis, ranging from measuring the optical properties of fruits to meat, liquid (milk) or colloidal food and other materials, 2,3 and evaluating the composition and quality of these products. 4,5 It can also be used for solution detection. Li et al 6 proposed a method for analysing the composition of mixed solutions with spatially resolved diffuse reflectance spectroscopy (SRDRS).…”

Section: Introductionmentioning

confidence: 99%

Segmented modelling and analysis of disperse dye concentration based on multidimensional spectrum

Zhang

Huang

Zheng

et al. 2023

Coloration Technology

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Since the water‐ insoluble dispersing dye has both absorption and scattering characteristics, a spatial resolution hyperspectral measurement approach and experimental testing was proposed in this paper, which can collect spectral and spatial data from samples simultaneously. The concentration of 81 groups of three‐component disperse dye samples were measured. However, the hyperspectral data of dye solutions in the 420‐800 nm band is saturated, resulting in the inability for multispectral data processing. A segmented concentration quantitative analysis model was developed. For the unsaturated band (420‐510 nm), the PLS, NPLS, and SVM models using the data points on X axis of Two‐dimensional light intensity distribution map were established. The predicted performance of PLS model was worse slightly than that of the other two models, The R2 values of concentrations for red, orange and blue disperse dye were 0.888, 0.796 and 0.959, respectively. For saturated band (520–670 nm), the NPLS and SVM models using the data points on X and Y axis were established. Results shows that the prediction accuracy of concentrations of the three‐component disperse dye was increased by adding additional data points on Y axis, with R2 values of 0.944, 0.807, and 0.912, respectively. For the strong scattering band (680–800 nm), a SVM model were established, and R2 of concentration of the three dyes reached 0.974, 0.933 and 0.995, respectively. The results showed that multi‐dimensional spectroscopy method can improve the prediction accuracy of component concentration of disperse dye solution, by using more spectral information from X and Y directions.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%

Segmented modelling and analysis of disperse dye concentration based on multidimensional spectrum

Zhang

Huang

Zheng

et al. 2023

Coloration Technology

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“…Sensors 2022, 22, 4378 2 of 12 multi-spectral images [7,8], and so on, are widely used to develop the crop monitoring system, and these methods have been known as powerful tools for monitoring of various agricultural products. In particular, several studies have been conducted to classify the tomato maturity based on digital images [9,10], spatially resolved spectra [11], and so on, but most studies were considered at the laboratory level.…”

Section: Introductionmentioning

confidence: 99%

Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

Cho

Kim

Lee

et al. 2022

Sensors

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It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.

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“…As an internal detection technique, near-infrared spectroscopy (NIRS) has been widely used to identify the geographical and botanical origin of agricultural products [13], particularly nuts, e.g., hazelnut [14][15][16], macadamia nut [17], walnut [18,19], and pine nut [20]. However, NIRS cannot obtain the information in the whole sample space due to single-point detection [21].…”

Section: Introductionmentioning

confidence: 99%

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Jiang

et al. 2021

Agriculture

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The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

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Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy

Cited by 21 publications

References 31 publications

Segmented modelling and analysis of disperse dye concentration based on multidimensional spectrum

Segmented modelling and analysis of disperse dye concentration based on multidimensional spectrum

Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

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