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Open AccessArticle
Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy
by
Min Hao
Min Hao 1,†,
Mengyu Zhang
Mengyu Zhang 2,†,
Haiqing Tian
Haiqing Tian 1,* and
Jianying Sun
Jianying Sun 1
1
Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
2
Xilingol Power Branch of Inner Mongolia Power (Group) Co., Xilinhot 026000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2024, 14(9), 1484; https://doi.org/10.3390/agriculture14091484 (registering DOI)
Submission received: 24 June 2024
/
Revised: 26 August 2024
/
Accepted: 26 August 2024
/
Published: 1 September 2024
Abstract
Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data,including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection.
Share and Cite
MDPI and ACS Style
Hao, M.; Zhang, M.; Tian, H.; Sun, J.
Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy. Agriculture 2024, 14, 1484.
https://doi.org/10.3390/agriculture14091484
AMA Style
Hao M, Zhang M, Tian H, Sun J.
Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy. Agriculture. 2024; 14(9):1484.
https://doi.org/10.3390/agriculture14091484
Chicago/Turabian Style
Hao, Min, Mengyu Zhang, Haiqing Tian, and Jianying Sun.
2024. "Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy" Agriculture 14, no. 9: 1484.
https://doi.org/10.3390/agriculture14091484
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