Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Su, Zhen-Zhu; Zhang, Chu; Yan, Tianying; Zhu, Jianan; Zeng, Yulan; Lu, Xuan-Jun; Gao, Pan; Liu, Feng; He, Linxin; Fan, Ling

doi:10.3389/fpls.2021.736334

Cited by 33 publications

(19 citation statements)

References 55 publications

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“…Due to the convex surface of the samples, the uneven reflection creates a highlighted region near the vertical axial as shown in Figure 2A . Thus, we use ENVI5.3 (ITT, Visual Information Solutions, Boulder, CO, USA) (Su et al, 2021 ) to avoid the highlight region and extract the reflection value for each band from the region of interest (Xue, 2010 ; Fu et al, 2021 ; Figure 2B ). The processed cherry tomato samples and the corresponding hyperspectral images are divided into training set, validation set, and test set with ratio of 7:1:2, respectively.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Xiang

Chen

et al. 2022

Front. Plant Sci.

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Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

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Section: Methodsmentioning

confidence: 99%

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Xiang

Chen

et al. 2022

Front. Plant Sci.

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“…After the physical indicators of the sample were measured, the chemical indicators of the samples were determined. The digital Abbe refractometer method [ 26 ] was used to determine the soluble solid content, and the 0.1 mol·L −1 NaOH titration method [ 27 ] was used to determine the titratable acid content.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Wei

et al. 2022

Plant Phenomics

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To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R,G,B,H,S,and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc<0.7) and a mature stage (after which color changes occurred) (0.7≤Mc<1). When predicting grape maturity based on the R,G,B,H,I,and S color values, the R,G,and I as well as G,H,and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc’s predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

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“…In this study, five-fold cross-validation was adopted for PLS and SVR models. Recently, it has been extended to analyzing multidimensional data [15,40]. In this study, we made a simple modification based on the method proposed in Feng's study [15] and made it suitable for regression problems.…”

Section: Conventional Regression Modelsmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

Self Cite

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Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

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Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Cited by 33 publications

References 55 publications

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

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