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Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems

Author

Listed:
  • Miltiadis Iatrou

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • Christos Karydas

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • George Iatrou

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • Ioannis Pitsiorlas

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • Vassilis Aschonitis

    (Soil and Water Resources Institute, Hellenic Agricultural Organization—Demeter, 57001 Thermi, Greece)

  • Iason Raptis

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • Stelios Mpetas

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

  • Kostas Kravvas

    (NLG Worldwide, Nikiforou Ouranou 3, 54627 Thessaloniki, Greece)

  • Spiros Mourelatos

    (Ecodevelopment S.A., 57010 Thessaloniki, Greece)

Abstract

This research is an outcome of the R&D activities of Ecodevelopment S.A. (steadily supported by the Hellenic Agricultural Organization—Demeter) towards offering precision farming services to rice growers. Within this framework, a new methodology for topdressing nitrogen prediction was developed based on machine learning. Nitrogen is a key element in rice culture and its rational management can increase productivity, reduce costs, and prevent environmental impacts. A multi-source, multi-temporal, and multi-scale dataset was collected, including optical and radar imagery, soil data, and yield maps by monitoring a 110 ha pilot rice farm in Thessaloniki Plain, Greece, for four consecutive years. RapidEye imagery underwent image segmentation to delineate management zones (ancillary, visual interpretation of unmanned aerial system scenes was employed, too); Sentinel-1 (SAR) imagery was modelled with Computer Vision to detect inundated fields and (through this) indicate the exact growth stage of the crop; and Sentinel-2 image data were used to map leaf nitrogen concentration (LNC) exactly before topdressing applications. Several machine learning algorithms were configured to predict yield for various nitrogen levels, with the XGBoost model resulting in the highest accuracy. Finally, yield curves were used to select the nitrogen dose maximizing yield, which was thus recommended to the grower. Inundation mapping proved to be critical in the prediction process. Currently, Ecodevelopment S.A. is expanding the application of the new method in different study areas, with a view to further empower its generality and operationality.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:4:p:312-:d:529593
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    References listed on IDEAS

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    1. Bouman, B.A.M. & Kropff, M.J. & Wopereis, M.C.S. & ten Berge, H.F.M. & van Laar, H.H., 2001. "ORYZA2000: modeling lowland rice," IRRI Books, International Rice Research Institute (IRRI), number 281825.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Vassilis Aschonitis & Christos G. Karydas & Miltos Iatrou & Spiros Mourelatos & Irini Metaxa & Panagiotis Tziachris & George Iatrou, 2019. "An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems," Agriculture, MDPI, vol. 9(4), pages 1-25, April.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Yang Ruan & Yakov Kuzyakov & Xiaoyu Liu & Xuhui Zhang & Qicheng Xu & Junjie Guo & Shiwei Guo & Qirong Shen & Yunfeng Yang & Ning Ling, 2023. "Elevated temperature and CO2 strongly affect the growth strategies of soil bacteria," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. 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.
    3. 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.
    4. Dong, Liming & Lei, Guoqing & Huang, Jiesheng & Zeng, Wenzhi, 2023. "Improving crop modeling in saline soils by predicting root length density dynamics with machine learning algorithms," Agricultural Water Management, Elsevier, vol. 287(C).

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