Authors
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Huihe Yang
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Jie Li
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Guifang Wu
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Xuehong De
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Yong Zhang
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Fang Guo
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Shubin Yan
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China; Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot, 010018, P.R. China
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Xiangping Bai
Inner Mongolia Autonomous Region Agricultural and Pastoral Technology Extension Center, Hohhot, 010010, P.R. China
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Haowen Xiao
Inner Mongolia Autonomous Region Agricultural and Pastoral Technology Extension Center, Hohhot, 010010, P.R. China
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Yang Cao
Inner Mongolia Autonomous Region Agricultural and Pastoral Technology Extension Center, Hohhot, 010010, P.R. China
Keywords:
Alfalfa hay, Image processing, Near-infrared spectroscopy, Electronic nose, Machine learning
Abstract
The quality detection of alfalfa hay is crucial for the development of animal husbandry. In this study, a method for quality detection of alfalfa hay based on the fusion of multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose is proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme and Successive Projections Algorithm from the spectral data. The image data were denoised using adaptive wavelet thresholding, and color and texture features were extracted using color histograms and random forest algorithms, respectively. Electronic nose data using principal component analysis was used for data dimensionality reduction. Support Vector Machine, Extreme Learning Machine, and Multi-Layer Perceptron were employed to establish quality detection models of alfalfa hay based on spectroscopy, image, gas information, and their combination, respectively. Experimental results demonstrate that the fusion of near-infrared spectroscopy, image data, and gas information effectively enhances the classification accuracy of the model. The accuracy of the test set reaches 100%, with root mean square error and determination coefficient values of 0.1728 and 0.9239, respectively, surpassing prediction models established solely on individual information. This study provides new insights into alfalfa hay quality detection.
