IoT 23(1): e1

Research Article

Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index

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  • @ARTICLE{10.4108/eetiot.v9i1.2904,
        author={Qi Liu and Yaxin Wang and Jie Yang and Wuping Zhang and Huanchen Wang and Fuzhong Li and Guofang Wang and Yuansen Huo and Jiwan Han},
        title={Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2023},
        month={3},
        keywords={Sorghum, Aboveground Biomass, UAV, Multispectral Image, Spatial Resolution},
        doi={10.4108/eetiot.v9i1.2904}
    }
    
  • Qi Liu
    Yaxin Wang
    Jie Yang
    Wuping Zhang
    Huanchen Wang
    Fuzhong Li
    Guofang Wang
    Yuansen Huo
    Jiwan Han
    Year: 2023
    Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index
    IOT
    EAI
    DOI: 10.4108/eetiot.v9i1.2904
Qi Liu1,*, Yaxin Wang1, Jie Yang1, Wuping Zhang1, Huanchen Wang1, Fuzhong Li1, Guofang Wang1, Yuansen Huo1, Jiwan Han1
  • 1: Shanxi Agricultural University
*Contact email: 297341853@163.com

Abstract

This work aims to explore the feasibility of predicting and estimating the aboveground biomass (AGB) of sorghum using multispectral images captured by UAVs, and clarify the quantitative relationship between vegetation index and sorghum AGB based on different spatial resolutions, and build an AGB estimation model based on UAV multispectral images and vegetation index under different spatial resolutions. Combining spatial resolution, vegetation index, and machine learning, a training set is used to train the model, and a verification set is used to verify the model to select the best prediction model corresponding to different spatial resolutions. The three best prediction models under three spatial resolutions are classic machine learning models. 1) when the spatial resolution is 0.017m, the model precision obtained from the random forest is R2=0.8961, MAE=26.4340, and RMSE=32.2459. 2) when the spatial resolution is 0.024m, the model accuracy obtained by the Lasso algorithm is R2=0.8826, MAE=31.106, and RMSE=40.2937; 3) when the spatial resolution is 0.030m, the model accuracy obtained by the decision tree algorithm is R2=0.8568, MAE=30.3373, and RMSE=40.8082; and 4) the model's accuracy decreases with the decrease of spatial resolution. The results show that the combination of spatial resolution, vegetation index, and machine learning algorithm is an effective, fast, and accurate prediction method.