数字农科院2.0

Enhancing rice phenology identification by synergistic learning canopy optical signals and plant height dynamics

文献类型： 外文期刊

作者： Ziqiu Li;Weiyuan Hong;Xiangqian Feng;Aidong Wang;Hengyu Ma;Ruijie Li;Qing Yao;Hao Jiang;Song Chen

作者机构：

关键词： Deep learning;Image classification;Rice phenology

期刊名称： Smart Agricultural Technology

ISSN： 2772-3755

年卷期： 2025 年 11 卷

页码：

收录情况： ESCI（2025版）

摘要： Accurate monitoring of rice phenological transitions plays a pivotal role in enhancing breeding efficiency and optimizing agronomic practices. Current spectral-based approaches frequently encounter limitations in detecting subtle growth stage boundaries within large-scale breeding programs, particularly due to visually imperceptible canopy variations during critical transitional phases. To address this issue, this study introduces a deep learning framework named GrowAI that synergistically combines dynamic plant architecture parameters with hyperspectral canopy signatures for robust phenological identification. Through a two-year breeding experiment, we established a time-series multispectral image dataset covering complete growth cycles. Our methodology innovatively integrates three-dimensional plant height dynamics with canopy optical properties through multimodal fusion architecture. Experimental results demonstrated GrowAI's superior performance, achieving classification accuracies of 0.937 (OA) and 0.927 (F1-score), representing average improvements of 6.9 % and 7.0 % respectively over conventional full-spectrum deep learning approaches. Notably, the framework exhibited exceptional temporal generalizability with cross-year validation accuracy reaching 0.977. Moreover, by accurately tracking the phenological stages of different rice genotypes in the breeding trials, the GrowAI framework can help breeders identify climate-resilient cultivars that have the most suitable phenological characteristics.

分类号：

  • 相关文献

[1]A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. Guo feng YANG,Yong YANG,Zi kang HE,Xin yu ZHANG,Yong HE. 2022

[2]A Spectral Signature Shape-Based Algorithm for Landsat Image Classification. Chen, Yuanyuan,Duan, Si-Bo,Li, Zhao-Liang,Chen, Yuanyuan,Wang, Yanlong,Duan, Si-Bo,Li, Zhao-Liang,Wang, Quanfang,Wang, Yanlong,Xu, Miaozhong. 2016

[3]An Efficient Classification Method of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features. Xue, Xiaorong,Xue, Xiaorong,Di, Liping,Guo, Liying,Lin, Li,Guo, Liying. 2015

[4]A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis. Chen, Man,Jin, Chengqian,Ni, Youliang,Yang, Tengxiang,Xu, Jinshan. 2024

[5]GACDNet:Mapping winter wheat by generative adversarial cross-domain networks with transformer integration for zero-sample extraction. Chunyang Wang,Kai Li,Wei Yang,Xinbing Wang,Jian Wang,Zongze Zhao,Yanan Gu,Zhaozhao Xu. 2024

[6]A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage. Fubing Liao,Xiangqian Feng,Ziqiu Li,Danying Wang,Chunmei Xu,Guang Chu,Hengyu Ma,Qing Yao,Song Chen. 2024

[7]Generalized zero-shot pest and disease image classification based on causal gating model. Shansong Wang,Qingtian Zeng,Guiyuan Yuan,Weijian Ni,Chao Li,Hua Duan,Nengfu Xie,Fengjin Xiao,Xiaofeng Yang. 2025

[8]TCSRNet: a lightweight tobacco leaf curing stage recognition network model. Panzhen Zhao,Songfeng Wang,Shijiang Duan,Aihua Wang,Lingfeng Meng,Yichong Hu. 2024

[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]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

[11]Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability. Lingbo Yang,Ran Huang,Jingcheng Zhang,Jingfeng Huang,Limin Wang,Jiancong Dong,Jie Shao. 2023

[12]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[13]A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence. Ruyu Mei,Kebiao Mao,Jiancheng Shi,Jeffrey Nielson,Sayed M. Bateni,Fei Meng,Guoming Du. 2023

[14]Machine learning for image-based multi-omics analysis of leaf veins. Yubin Zhang,Ning Zhang,Xiujuan Chai,Tan Sun. 2023

[15]Auction-based deep learning-driven smart agricultural supply chain mechanism. Yu Feng,Dong Mei,Hua Zhao. 2023

[16]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[17]Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Liangliang Zhang,Zhao Zhang,Yuchuan Luo,Juan Cao,Ruizhi Xie,Shaokun Li. 2021

[18]Deep learning for change detection in remote sensing: a review. Bai, Ting,Wang, Le,Yin, Dameng,Sun, Kaimin,Chen, Yepei,Li, Wenzhuo,Li, Deren. 2022

[19]Mapping of lakes in the Qinghai-Tibet Plateau from 2016 to 2021: trend and potential regularity. Zhichong Yang,Si Bo Duan,Xiaoai Dai,Yingwei Sun,Meng Liu. 2022

[20]Bionic technology and deep learning in agricultural engineering: Current status and future prospects. Chunlei Tu,Jinxia Li,Xingsong Wang,Shen Cheng,Jie Li. 2021

作者其他论文 更多>>