Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.


    Access

    Download


    Export, share and cite



    Title :

    Grain Crop Yield Prediction Using Machine Learning Based on UAV Remote Sensing: A Systematic Literature Review


    Contributors:
    Jianghao Yuan (author) / Yangliang Zhang (author) / Zuojun Zheng (author) / Wei Yao (author) / Wensheng Wang (author) / Leifeng Guo (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data

    Shurong Yang / Lei Li / Shuaipeng Fei et al. | DOAJ | 2024

    Free access

    Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

    Zhen Cao / Lammert Kooistra / Wensheng Wang et al. | DOAJ | 2023

    Free access

    Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle

    LIU FEI / KONG WENWEN / HE YONG et al. | European Patent Office | 2020

    Free access

    INTEGRATED REMOTE SENSING TOOLS FOR TIMELY PREDICTIONS OF CROP QUALITY AND YIELD

    NOLAND REAGAN / WELLS SCOTT / SHEAFFER CRAIG | European Patent Office | 2022

    Free access

    Integrated remote sensing tools for timely predictions of crop quality and yield

    NOLAND REAGAN / WELLS SCOTT / SHEAFFER CRAIG | European Patent Office | 2021

    Free access