数字农科院2.0

Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion

文献类型： 外文期刊

作者： Chen, Bingnan;Su, Qihe;Li, Yansong;Chen, Rui;Yang, Wanneng;Huang, Chenglong

作者机构：

关键词： （1-1-3）unmanned aerial vehicle;vegetation indices;deep learning and machine learning;feature fusion;fertilization management

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 4 期

页码：

收录情况： SCIE（2025版）

摘要： Rice, as a globally vital staple crop, requires efficient field monitoring to ensure optimal growth conditions. This study proposed a novel framework for classifying nutrient deficiencies and formulating fertilization strategies in field-grown rice by fusing UAV-derived vegetation indices (VIs) with deep image features extracted via deep neural networks. The framework integrated visible light VIs, spectral VIs, and image features to provide a comprehensive reflection of crop nutritional conditions, aligning closely with practical production needs. The deep image features achieved nutrition classification accuracies of 88.78% and 84.56% for rice spikelet protection fertilizer application stage (S1) and bud-promoting fertilizer application stage (S2), while the fusion of VIs and deep image features significantly enhanced the accuracy of nutrient classification, with the RF model achieving the highest accuracy (97.50% in S1 and 96.56% in S2). The proposed fertilization strategy effectively improved rice growth traits, demonstrating the potential of UAV-based remote sensing for precision agriculture, which would provide a scalable solution for optimizing rice cultivation and ensuring food security.

分类号：

  • 相关文献

[1]Fruits-Based Critical Nitrogen Dilution Curve for Diagnosing Nitrogen Status in Cotton. Weina Feng,Xiaofei Li,Helin Dong,Yukun Qin,Miao Sun,Jingjing Shao,Cangsong Zheng,Pengcheng Li. 2022

[2]Application of controlled-release urea increases maize N uptake, environmental benefits and economic returns via optimizing temporal and spatial distributions of soil mineral N. SUN M.,LI J.,ZHANG L.,SU X.,LIU N.,HAN X.,WU S.,SUN Z.,YANG X.. 2024

[3]Long-term green manuring increases soil carbon sequestration via decreasing qCO2 caused by lower microbial phosphorus limitation in a dry land field. Zhengbo Ma,Ting Liang,Haoran Fu,Qingxu Ma,Danna Chang,Jiudong Zhang,Zongxian Che,Guopeng Zhou,Weidong Cao. 2024

[4]Noise-tolerant RGB-D feature fusion network for outdoor fruit detection. Qixin Sun,Xiujuan Chai,Zhikang Zeng,Guomin Zhou,Tan Sun. 2022

[5]Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion. Zhihao Lu,Shihao Huang,Xiaojun Zhang,Yuxuan shi,Wanneng Yang,Longfu Zhu,Chenglong Huang. 2023

[6]Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing. Weiguang Zhai,Changchun Li,Qian Cheng,Fan Ding,Zhen Chen. 2023

[7]Multimodal fusion-based detection method of estrus cows using multisource data inspired by hidden Markov model algorithms. Jun Wang,Yijia Zhao,Xiaoxia Li,Yu Zhou,Kaixuan Zhao,Hui Wang,Waleid Mohamed EL-Sayed Shakweer. 2025

[8]An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation. Xiaofei Dai,Guodong Cheng,Lu Yang,Yali Wang,Zhongkun Li,Shuqing Han,Jifang Liu. 2025

[9]The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion. Panzhen Zhao,Shijiang Duan,Songfeng Wang,Aihua Wang,Lingfeng Meng,Zhicheng Wang,Yingpeng Dai. 2025

[10]Detection of Fusarium Head Blight in Individual Wheat Spikes Using Monocular Depth Estimation with Depth Anything V2. Jiacheng Wang,Jianliang Wang,Yuanyuan Zhao,Fei Wu,Wei Wu,Zhen Li,Chengming Sun,Tao Li,Tao Liu. 2025

[11]Research on the Classification Method of Fresh Tobacco Leaf Maturity Based on Transfer Learning and Multi-Feature Fusion. Zhao, Panzhen,Dai, Yingpeng. 2025

[12]BTSM-UNet: Bidirectional Temporal-Spatial vision Mamba-UNet for winter wheat classification based on Sentinel-2 imagery. Fan, Lingling,Chen, Shi,Xia, Lang,Zha, Yan,Zhou, Qingbo,Yan, Qi. 2025

[13]Estimation of nitrogen status in middle and bottom layers of winter wheat canopy by using ground-measured canopy reflectance. Wang, ZJ,Wang, JH,Liu, LY,Huang, WJ,Zhao, CJ,Lu, YL. 2005

[14]Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance. Zhou, Yilin,Duan, Xiayu,Cheng, Dengfa,Cao, Xueren,Luo, Yong.

[15]Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs. Yang Liu,Haikuan Feng,Jibo Yue,Yiguang Fan,Xiuliang Jin,Xiaoyu Song,Hao Yang,Guijun Yang. 2022

[16]A comparison of methods to estimate leaf area index using either crop-specific or generic proximal hyperspectral datasets. Chenwei Nie,Lei Shi,Zhenhai Li,Xiaobin Xu,Dameng Yin,Shaokun Li,Xiuliang Jin. 2022

[17]Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment. Qi Qin,Dawei Xu,Lulu Hou,Beibei Shen,Xiaoping Xin. 2021

[18]Predicting equivalent water thickness in wheat using UAV mounted multispectral sensor through deep learning techniques. Adama Traore,Syed Tahir Ata-Ul-karim,Aiwang Duan,Mukesh Kumar Soothar,Seydou Traore,Ben Zhao. 2021

[19]Stacking of Canopy Spectral Reflectance from Multiple Growth Stages Improves Grain Yield Prediction under Full and Limited Irrigation in Wheat. Hassan, Muhammad Adeel,Fei, Shuaipeng,Li, Lei,Jin, Yirong,Liu, Peng,Rasheed, Awais,Shawai, Rabiu Sani,Zhang, Liang,Ma, Aimin,Xiao, Yonggui,He, Zhonghu. 2022

[20]Quantifying effect of maize tassels on LAI estimation based on multispectral imagery and machine learning methods. Mingchao Shao,Chenwei Nie,Aijun Zhang,Liangsheng Shi,Yuanyuan Zha,Honggen Xu,Hongye Yang,Xun Yu,Yi Bai,Shuaibing Liu,Minghan Cheng,Tao Lin,Ningbo Cui,Wenbin Wu,Xiuliang Jin. 2023

作者其他论文 更多>>