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Convolutional Neural Network-Based Estimation of Nitrogen Content in Regenerating Rice Leaves

文献类型： 外文期刊

作者： Hu, Tian 1 ; Liu, Zhihua 1 ; Hu, Rong 2 ; Tian, Mi 3 ; Wang, Zhiwei 1 ; Li, Ming 1 ; Chen, Guanghui 4 ;

作者机构： 1.Hunan Acad Agr Sci, Hunan Agr Equipment Res Inst, Changsha 410011, Peoples R China

2.Hunan Agr Univ, Sch Food Sci & Technol, Changsha 410128, Peoples R China

3.Hunan Univ Humanities Sci & Technol, Sch Educ, Loudi 417000, Peoples R China

4.Hunan Agr Univ, Coll Agron, Changsha 410128, Peoples R China

关键词： nitrogen content prediction; regenerating rice; hyperspectral imaging; convolutional neural network

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 7 期

页码：

收录情况： SCI

摘要： Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement.

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