Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems

Miltiadis Iatrou, Christos Karydas, George Iatrou, Ioannis Pitsiorlas, Vassilis Aschonitis, Iason Raptis, Stelios Mpetas, Kostas Kravvas and Spiros Mourelatos
Additional contact information
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

Agriculture, 2021, vol. 11, issue 4, 1-17

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.

Keywords: precision agriculture; RapidEye imagery; sentinel imagery; yield maps; XGBoost (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2077-0472/11/4/312/pdf (application/pdf)
https://www.mdpi.com/2077-0472/11/4/312/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:11:y:2021:i:4:p:312-:d:529593

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().