数字农科院2.0

Visible-near infrared hyperspectral imaging for non-destructive estimation of leaf nitrogen content under water-saving irrigation in protected tomato cultivation

文献类型： 外文期刊

作者： Hu, Caixia;Zhao, Tingting;Duan, Yingying;Zhang, Yungui;Wang, Xinxiu;Li, Jie;Zhang, Guilong

作者机构：

关键词： tomato;leaf nitrogen content (LNC);hyperspectral imaging;water-saving irrigation;machine learning

期刊名称： FRONTIERS IN PLANT SCIENCE

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCIE（2025版）

摘要： Accurate estimation of leaf nitrogen content (LNC) is critical for optimizing fertilization strategies in greenhouse tomato production. This study developed a robust hyperspectral-based framework for non-destructive LNC prediction by combining advanced spectral preprocessing, feature selection, and machine learning. Hyperspectral reflectance data were collected across five nitrogen and irrigation treatments over key growth stages. Signal quality was enhanced through Savitzky-Golay smoothing (SG) and Standard Normal Variate normalization (SNV). Key nitrogen-sensitive wavelengths-centered around 725 nm and 730 - 780 nm-were identified using Competitive Adaptive Reweighted Sampling (CARS) and Principal Component Analysis (PCA). Four predictive models were compared, among which a hybrid Stacked Autoencoder-Feedforward Neural Network (SAE-FNN) achieved the highest accuracy (test R-2 = 0.77, RPD = 2.06), effectively capturing nonlinear spectral-nitrogen interactions. In contrast, Support Vector Machine (SVM) exhibited overfitting and Partial Least Squares Method (PLSR) underperformed due to its linear constraints. These results underscore the potential of integrating hyperspectral sensing with deep learning for intelligent nitrogen monitoring in controlled-environment agriculture.

分类号：

  • 相关文献

[1]A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging. Yuanyuan Shao,Yukang Shi,Yongdong Qin,Guantao Xuan,Jing Li,Quankai Li,Fengjuan Yang,Zhichao Hu. 2022

[2]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[3]Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds. Tingting Zhang,Long Lu,Ni Yang,Ian D. Fisk,Wensong Wei,Li Wang,Jing Li,Qun Sun,Rensen Zeng. 2023

[4]Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review. Junjie Ma,Bangyou Zheng,Yong He. 2022

[5]A nondestructive method for determination of green tea quality by hyperspectral imaging. Yu Tang,Fan Wang,Xiaoqing Zhao,Guijun Yang,Bo Xu,Ying Zhang,Ze Xu,Haibin Yang,Lei Yan,Long Li. 2023

[6]Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish. Yi Ming Cao,Yan Zhang,Qi Wang,Ran Zhao,Mingxi Hou,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Xiao Qing Sun,Shijing Liu,Jiong Tang Li. 2024

[7]Prediction of Vigor of Naturally Aged Seeds from Xishuangbanna Cucumber (Cucumis sativus L. var. xishuangbannanesis) Using Hyperspectral Imaging. Zhang, Meng,Song, Jiangping,Jia, Huixia,Zhang, Xiaohui,Yang, Wenlong,Wang, Yang,Wang, Haiping. 2025

[8]番茄基因组中抗病基因同源序列分离. 时涛,孙福在,回文广,李国庆. 2002

[9]蔬菜育种的基因组学. 黄三文. 2012

[10]感细菌性斑点病番茄品种上存有抗病基因pto序列. 时涛,李国庆,孙福在. 2004

[11]李宝聚博士诊病手记(八十八)番茄细菌性斑点病症状多样性与综合防治. 王莹莹,柴阿丽,李宝聚. 2015

[12]A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning. Riqiang Chen,Wenping Liu,Hao Yang,Xiuliang Jin,Guijun Yang,Yan Zhou,Chengjian Zhang,Shaoyu Han,Yang Meng,Changyuan Zhai,Haikuan Feng. 2024

[13]Adaptation strategies of climate variability impacts on alpine grassland ecosystems in Tibetan Plateau. Gao, Qing-zhu,Li, Yue,Wan, Yun-fan,Gao, Qing-zhu,Li, Yue,Wan, Yun-fan,Xu, Hong-mei,Jiangcun, Wang-zha. 2014

[14]Cost-effectiveness analysis of water-saving irrigation technologies based on climate change response: A case study of China. Zou, Xiaoxia,Li, Yu'e,Gao, Qingzhu,Wan, Yunfan,Qin, Xiaobo,Cremades, Roger,Cremades, Roger.

[15]Multi-objective deficit drip irrigation optimization of citrus yield, fruit quality and water use efficiency using NSGA-II in seasonal arid area of Southwest China. Fei Chen,Ningbo Cui,Shouzheng Jiang,Zhihui Wang,Hongping Li,Min Lv,Yaosheng Wang,Daozhi Gong,Lu Zhao. 2023

[16]Potential of optimizing irrigation and fertilization management for sustainable rice production in China. Lianhua Liu,Zhongmin Lian,Wei Ouyang,Lingling Yan,Hongbin Liu,Fanghua Hao. 2023

[17]Effect of Delayed Irrigation at the Jointing Stage on Nitrogen, Silicon Nutrition and Grain Yield of Winter Wheat in the North China Plain. Hao Zheng,Jinyang Sun,Yueping Liang,Caiyun Cao,Yang Gao,Junpeng Zhang,Hongkai Dang,Chunlian Zheng. 2024

[18]Water-saving irrigation suppresses on soil-crop pathogenicity: Insight on the co-host of virulence factors and antibiotic resistance genes. Lizhi Zhao,Yuhui Li,Qifan Yang,Ruolan Li,Houyu Li,Yan Xu. 2025

[19]Investigating Sedimentation Patterns and Fluid Movement in Drip Irrigation Emitters in the Yellow River Basin. Mengyang Wang,Mengyun Xue,Hao Sun,Hui Li,Rui Li,Qibiao Han. 2025

[20]Relationships between C/N metabolism and rice growth related indicators under alternating drought and flooding stress. Zhenchang Wang,Yuexiong Wang,Rangjian Qiu,Xiangping Guo,Bingqi Xiao,Jinjing Liu,Cheng Hong,Minghao Tian,Xiaoman Qiang. 2024

作者其他论文 更多>>