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Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning

Author

Listed:
  • Fan Ding

    (Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

  • Changchun Li

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

  • Weiguang Zhai

    (Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

  • Shuaipeng Fei

    (Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Qian Cheng

    (Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Zhen Chen

    (Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

Abstract

Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform with multiple sensors to estimate the N content of winter wheat using machine learning algorithms; to collect multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) images to construct a multi-source data fusion dataset; to predict the N content in winter wheat using random forest regression (RFR), support vector machine regression (SVR), and partial least squares regression (PLSR). The results showed that the mean absolute error (MAE) and relative root-mean-square error (rRMSE) of all models showed an overall decreasing trend with an increasing number of input features from different data sources. The accuracy varied among the three algorithms used, with RFR achieving the highest prediction accuracy with an MAE of 1.616 mg/g and rRMSE of 12.333%. For models built with single sensor data, MS images achieved a higher accuracy than RGB and TIR images. This study showed that the multi-source data fusion technique can enhance the prediction of N content in winter wheat and provide assistance for decision-making in practical production.

Suggested Citation

  • Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1752-:d:950899
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    References listed on IDEAS

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    1. Daniele Masseroni & Bianca Ortuani & Martina Corti & Pietro Marino Gallina & Giacomo Cocetta & Antonio Ferrante & Arianna Facchi, 2017. "Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels," Sustainability, MDPI, vol. 9(9), pages 1-20, August.
    2. Miltiadis Iatrou & Christos Karydas & George Iatrou & Ioannis Pitsiorlas & Vassilis Aschonitis & Iason Raptis & Stelios Mpetas & Kostas Kravvas & Spiros Mourelatos, 2021. "Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems," Agriculture, MDPI, vol. 11(4), pages 1-17, April.
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    4. Elsayed, Salah & Elhoweity, Mohamed & Ibrahim, Hazem H. & Dewir, Yaser Hassan & Migdadi, Hussein M. & Schmidhalter, Urs, 2017. "Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 189(C), pages 98-110.
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    Cited by:

    1. Changchun Li & Xinyan Li & Xiaopeng Meng & Zhen Xiao & Xifang Wu & Xin Wang & Lipeng Ren & Yafeng Li & Chenyi Zhao & Chen Yang, 2023. "Hyperspectral Estimation of Nitrogen Content in Wheat Based on Fractional Difference and Continuous Wavelet Transform," Agriculture, MDPI, vol. 13(5), pages 1-25, May.

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