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Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network

文献类型： 外文期刊

作者： Zhang, Liu 1 ; An, Dong 1 ; Wei, Yaoguang 1 ; Liu, Jincun 1 ; Wu, Jianwei 5 ;

作者机构： 1.China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing 100083, Peoples R China

2.China Agr Univ, Key Lab Smart Farming Technol Aquat Anim & Livest, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China

3.Beijing Engn & Technol Res Ctr Internet Things Ag, Beijing 100083, Peoples R China

4.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

5.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Near infrared hyperspectral imaging; Oil content; Prediction; Deep learning; Attentional mechanism

期刊名称：FOOD CHEMISTRY （ 2021影响因子：9.231； 五年影响因子：8.795 ）

ISSN： 0308-8146

年卷期： 2022 年 395 卷

页码：

收录情况： SCI

摘要： An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.

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