Prediction of peanut seed vigor based on hyperspectral images

Zou, Zhiyong; Chen, Jie; Zhou, Man; Zhao, Yongpeng; Long, Taotao; WU, Qingsong; Xu, Liang

doi:10.1590/fst.32822

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Meanwhile, hyperspectral images are highly correlated between adjacent wavebands, which leads to collinearity and redundancy problems. Optimal wavelength selection algorithm needed to be used to solve the former problem, in order to shorten the time of building prediction models, reduce the dimension of spectral data and promote the performance of prediction models (Zou et al, 2022a(Zou et al, , 2022b.…”

Section: Optimal Wavelength Selectionmentioning

confidence: 99%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, with 2 p R and p RMSE on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for 2 p R and p RMSE , respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf.

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Section: Optimal Wavelength Selectionmentioning

confidence: 99%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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“…Mesa & Chiang (2021) used hyperspectral imaging technology combined with RGB to classify bananas (Mesa & Chiang, 2021). Zou et al (2022) used hyperspectral nondestructive testing technology to predict peanut seed vigor with high accuracy (Zou et al, 2022). Chen et al (2022) used hyperspectral technology combined with the inversion model of LS-SVM to achieve rapid online monitoring of soybean breakage rate by combine harvesters (Chen et al, 2022).…”

Section: Identification Of Peanut Storage Period Based On Hyperspectr...mentioning

confidence: 99%

Identification of peanut storage period based on hyperspectral imaging technology

et al. 2022

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Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

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“…Hyperspectral imaging technology has been applied in many fields, such as peanut (Zou et al, 2022b) germination prediction (Zou et al, 2022c), mildew detection (Zou et al, 2022a), hot pot (Zou et al, 2023) oil detection (Zou et al, 2022d), fruit grading (Zou et al, 2021), etc. Using physical and chemical methods to judge the quality of honey (Kek et al, 2017;Wan et al, 2018), the results are often very accurate (Shamsudin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Honey quality detection based on near-infrared spectroscopy

et al. 2023

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As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

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Prediction of peanut seed vigor based on hyperspectral images

Cited by 5 publications

References 29 publications

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Identification of peanut storage period based on hyperspectral imaging technology

Honey quality detection based on near-infrared spectroscopy

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