Moisture Content Detection of Maize Kernels Based on Hyperspectral Imaging Technology and CARS

Chaopeng, 王超鹏 Wang; Wenqian, 黄文倩 Huang; Shuxiang, 樊书祥 Fan; Baohua, 张保华 Zhang; Chen, 刘宸 Liu; Xiaobin, 王晓彬 Wang; Liping, 陈立平 Chen

doi:10.3788/lop53.123001

Cited by 2 publications

(1 citation statement)

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“…Most of the above method is mainly based on the classification of the qualitative analysis for corn seed vigor, such as the microwave heat treatment and the nontreated seed separation [33], seed aging degree evaluation [34], viable and nonviable discrimination [35], vitality level prediction [36], hardness, springiness, and resilience prediction [38]. The quantitative analysis mainly focused on the prediction of seed components related to vitality [37,39]. There is less research which has been conducted on the quantitative analysis of vigor for corn seeds, especially for sweet corn seeds.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Cui

Cheng

et al. 2020

Sensors

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Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400–1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.

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Section: Introductionmentioning

confidence: 99%

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Cui

Cheng

et al. 2020

Sensors

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Moisture Content Detection of Soybean Grains Based on Hyperspectral Imaging

CHANG,

CHEN,

CHENG

et al. 2024

INMATEH

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Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

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Moisture Content Detection of Maize Kernels Based on Hyperspectral Imaging Technology and CARS

Cited by 2 publications

References 0 publications

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Moisture Content Detection of Soybean Grains Based on Hyperspectral Imaging

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