文献类型： 外文期刊

作者： Chen, Riqiang ¹ ; Liu, Wenping ¹ ; Yang, Hao ⁴ ; Jin, Xiuliang ⁴ ; Yang, Guijun ² ; Zhou, Yan ¹ ; Zhang, Chengjian ¹ ; Han, Shaoyu ² ; Meng, Yang ² ; Zhai, Changyuan ⁶ ; Feng, Haikuan ² ;

作者机构： 1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Natl Forestry & Grassland Adm, Engn Res Ctr Forestry Oriented Intelligent Informa, Beijing 100083, Peoples R China

4.Chinese Acad Agr Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Inst Crop Sci, Beijing 100081, Peoples R China

5.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Jiangsu, Peoples R China

6.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

7.Henan Acad Agr Sci, Inst Agr Informat Technol, Zhengzhou 45000, Henan, Peoples R China

关键词： Apple; Leaf Nitrogen Content (LNC); Phenology; DNN; Data -Processing

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 219 卷

页码：

收录情况： SCI

摘要： Rapid and accurate assessment of apple-tree leaf nitrogen content (LNC) plays an important role in smart orchards for accurate fertilization. Hyperspectral remote sensing technology provides a reliable, economical, and fast method to estimate LNC. However, high-dimensional spectral data contain redundant information that is irrelevant to the target parameters, necessitating the development of robust hyperspectral analysis techniques to estimate apple-tree LNC. Additionally, the role of phenological information in improving LNC estimates across multiple growth stages remains uncertain. The present study thus proposes a novel hyperspectral data-processing framework (Boruta-Iteration-DNN) to optimize estimates of apple-tree LNC and investigates the potential of phenological information to improve LNC estimates of multi-fertility apple trees. The proposed framework incorporates the Boruta feature-selection method for identifying important spectral bands and assessing how they affect model performance through iterative feature analysis. A deep neural network (DNN) regression model is then developed for LNC estimation. In addition, a widely used Pearson-DNN method is used for comparison to demonstrate the superiority of the proposed approach. Furthermore, the potential of incorporating phenological information (Day After Anthesis, DAA) into the estimation models is examined to evaluate its ability to better estimate LNC. The results demonstrate that the proposed framework not only enhances the accuracy of apple-tree LNC estimates with respect to the full-spectrum model (without DAA: validation R2 improved from 0.69 to 0.75, NRMSE reduced from 11.92 % to 9.97 %; with DAA: validation R2 improved from 0.72 to 0.79, NRMSE reduced from 11.09 % to 9.06 %), but also reduces the complexity and dimensionality of the models by eliminating 96 % of the redundant spectral bands (2015 bands). Moreover, the inferior results of the Pearson-Iteration-DNN framework further underscore the effectiveness of the Boruta method in selecting relevant spectral bands. Additionally, including the DAA phenological information enhances the accuracy of all models (the total R2 of training and validation increased by 0.05 - 0.12, and the total NRMSE reduced by 1.49 % - 1.93 %). These findings highlight the ability of DNN models integrated with hyperspectral data and phenological information to accurately assess LNC in apple trees, thereby offering valuable guidance to orchard management for developing precise fertilization strategies.