Nondestructive phenotyping fatty acid trait of single soybean seeds using reflective hyperspectral imagery

Fu, Dandan; Zhou, Jianfeng; Scaboo, Andrew; Niu, Xiaofan

doi:10.1111/jfpe.13759

Cited by 18 publications

(9 citation statements)

References 27 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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“…Hyperspectral imaging (HSI) is an emerging analytical technology that combines spectral and imaging modalities to simultaneously acquire image and spectral information-typically continuous band images from the visible (Vis) to the nearinfrared (NIR) region-of the target object. HSI has been widely used in the qualitative and quantitative analysis of agricultural products (Fu et al, 2021;Zhu et al, 2020). For example, Jiang et al (2022) used NIR HSI to predict the total acid and reducing sugar contents in grains and visualize the distributions of these components.…”

Section: Introductionmentioning

confidence: 99%

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques

Liang,

Tian,

et al. 2024

Journal of Food Science

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Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible–near‐infrared (Vis–NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis–NIR, NIR, and the fused Vis–NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)‐AdaBoost and XGBoost models were built based on the low‐level fusion (LLF) and intermediate‐level fusion (ILF) of single Vis–NIR and NIR as well as the fused data. The results showed that the SVM‐AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

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“…In addition, for HSI data analysis, many soybean seed researchers have applied data preprocessing and machine learning algorithms, such as Savitzky-Golay (SG) preprocessing coupled with support vector machine (SVM) models for discriminating seed viability [14], MSC in conjunction with ensemble learning (EL) for soybean variety identification [15], SG and MSC with the partial least squares discrimination analysis (PLS-DA) method for fatty acid content classification in soybean seeds [16], SNV associated with partial least squares regression (PLSR) methods for predicting protein content [17], MSC with a one-dimensional convolutional neural network for identifying soybeans with high oil content [18], and the use of spectra without preprocessing with a PLSR model for determining soybean seed moisture content [19].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of the Vigor of Soybean Seeds Treated with Micronutrients Using X-ray Fluorescence Spectroscopy and Hyperspectral Imaging

Alves,

Gomes-Junior,

Carmo-Filho

et al. 2023

Agronomy

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Seed treatment with micronutrients is a crucial strategy for providing early seedling supply during development, and is commonly employed in soybean cultivation. However, responses to micronutrient treatment may vary based on seed vigor levels. Therefore, this study aimed to assess the potential of hyperspectral imaging combined with preprocessing and machine learning, compared to X-ray fluorescence spectroscopy, in evaluating the dynamics of micronutrient uptake during the germination of soybean seeds with varying levels of vigor. Two seed lots with differing levels of vigor were utilized for the analysis. The absorption of micronutrients by the seeds was evaluated using X-ray fluorescence spectroscopy (XRF), microprobe X-ray fluorescence spectroscopy (μ-XRF) and hyperspectral imaging (HSI) in two regions of interest (cotyledons and the embryonic axis). Artificial neural network (ANN), decision tree (DT) and partial least squares–discriminant analysis (PLS-DA) classification models, along with the Savitzky–Golay (SG), standard normal variation (SNV) and multiplicative scatter correction (MSC) methods, were employed to determine seed vigor based on the impact of micronutrient treatment. XRF identified higher concentrations of micronutrients in the treated seeds, with zinc being the predominant element. μ-XRF analysis revealed that a significant proportion of the micronutrients remained adhered to the hilum and seed coat, irrespective of seed vigor. The PLS-DA classification model using spectral data exhibited higher accuracy in classifying soybean seeds with high and low vigor, regardless of seed treatment with micronutrients and the analyzed region.

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Nondestructive phenotyping fatty acid trait of single soybean seeds using reflective hyperspectral imagery

Cited by 18 publications

References 27 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

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques

Evaluation of the Effect of the Vigor of Soybean Seeds Treated with Micronutrients Using X-ray Fluorescence Spectroscopy and Hyperspectral Imaging

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