IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i11p1752-d950899.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/11/1752/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/11/1752/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Klem, Karel & Záhora, Jaroslav & Zemek, František & Trunda, Petr & Tůma, Ivan & Novotná, Kateřina & Hodaňová, Petra & Rapantová, Barbora & Hanuš, Jan & Vavříková, Jana & Holub, Petr, 2018. "Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection," Agricultural Water Management, Elsevier, vol. 210(C), pages 171-184.
    3. 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.
    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.
    5. Umut Hasan & Mamat Sawut & Shuisen Chen, 2019. "Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters," Sustainability, MDPI, vol. 11(23), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.
    2. Min Yan & Yonghua Xia & Xiangying Yang & Xuequn Wu & Minglong Yang & Chong Wang & Yunhua Hou & Dandan Wang, 2023. "Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    3. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    4. Mwinuka, Paul Reuben & Mourice, Sixbert K. & Mbungu, Winfred B. & Mbilinyi, Boniphace P. & Tumbo, Siza D. & Schmitter, Petra, 2022. "UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: A case of Africa," Agricultural Water Management, Elsevier, vol. 266(C).
    5. Salah Elsayed & Mohamed Gad & Mohamed Farouk & Ali H. Saleh & Hend Hussein & Adel H. Elmetwalli & Osama Elsherbiny & Farahat S. Moghanm & Moustapha E. Moustapha & Mostafa A. Taher & Ebrahem M. Eid & M, 2021. "Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
    6. Yingying Xing & Xiaoli Niu & Ning Wang & Wenting Jiang & Yaguang Gao & Xiukang Wang, 2020. "The Correlation between Soil Nutrient and Potato Quality in Loess Plateau of China Based on PLSR," Sustainability, MDPI, vol. 12(4), pages 1-17, February.
    7. Wenfeng Li & Kun Pan & Wenrong Liu & Weihua Xiao & Shijian Ni & Peng Shi & Xiuyue Chen & Tong Li, 2024. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion," Agriculture, MDPI, vol. 14(8), pages 1-22, August.
    8. Zhang, Minne & Zhao, Weixia & Zhu, Changxin & Li, Jiusheng, 2024. "Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation," Agricultural Water Management, Elsevier, vol. 295(C).
    9. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    10. García, Ana Belén Mira & Romero-Trigueros, Cristina & Gambín, José María Bayona & Sánchez Iglesias, Ma del Puerto & Tortosa, Pedro Antonio Nortes & Nicolás, Emilio Nicolás, 2023. "Estimation of stomatal conductance by infra-red thermometry in citrus trees cultivated under regulated deficit irrigation and reclaimed water," Agricultural Water Management, Elsevier, vol. 276(C).
    11. Coelho, Anderson Prates & Faria, Rogério Teixeira de & Leal, Fábio Tiraboschi & Barbosa, José de Arruda & Dalri, Alexandre Barcellos & Rosalen, David Luciano, 2019. "Estimation of irrigated oats yield using spectral indices," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    12. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    13. Maria Victoria Bascon & Tomohiro Nakata & Satoshi Shibata & Itsuki Takata & Nanami Kobayashi & Yusuke Kato & Shun Inoue & Kazuyuki Doi & Jun Murase & Shunsaku Nishiuchi, 2022. "Estimating Yield-Related Traits Using UAV-Derived Multispectral Images to Improve Rice Grain Yield Prediction," Agriculture, MDPI, vol. 12(8), pages 1-28, August.
    14. Miltiadis Iatrou & Miltiadis Tziouvalekas & Alexandros Tsitouras & Elefterios Evangelou & Christos Noulas & Dimitrios Vlachostergios & Vassilis Aschonitis & George Arampatzis & Irene Metaxa & Christos, 2024. "Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference," Agriculture, MDPI, vol. 14(4), pages 1-18, March.
    15. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    16. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
    17. Melo, Leonardo Leite de & Melo, Verônica Gaspar Martins Leite de & Marques, Patrícia Angélica Alves & Frizzone, Jose Antônio & Coelho, Rubens Duarte & Romero, Roseli Aparecida Francelin & Barros, Timó, 2022. "Deep learning for identification of water deficits in sugarcane based on thermal images," Agricultural Water Management, Elsevier, vol. 272(C).
    18. Mohamed A. Darwish & Ahmed F. Elkot & Ahmed M. S. Elfanah & Adel I. Selim & Mohamed M. M. Yassin & Elsayed A. Abomarzoka & Maher A. El-Maghraby & Nazih Y. Rebouh & Abdelraouf M. Ali, 2023. "Evaluation of Wheat Genotypes under Water Regimes Using Hyperspectral Reflectance and Agro-Physiological Parameters via Genotype by Yield*Trait Approaches in Sakha Station, Delta, Egypt," Agriculture, MDPI, vol. 13(7), pages 1-20, June.
    19. Urs Feller & Stanislav Kopriva & Valya Vassileva, 2018. "Plant Nutrient Dynamics in Stressful Environments: Needs Interfere with Burdens," Agriculture, MDPI, vol. 8(7), pages 1-6, July.
    20. He Liu & Qinghui Zhu & Xiaomeng Xia & Mingwei Li & Dongyan Huang, 2021. "Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System," Agriculture, MDPI, vol. 12(1), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1752-:d:950899. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.