Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems

Iatrou, Miltiadis and Karydas, Christos and Iatrou, George and Pitsiorlas, Ioannis and Aschonitis, Vassilis and Raptis, Iason and Mpetas, Stelios and Kravvas, Kostas and Mourelatos, Spiros (2021) Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems. Agriculture, 11 (4). p. 312. ISSN 2077-0472

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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.

Item Type: Article
Subjects: European Repository > Agricultural and Food Science
Depositing User: Managing Editor
Date Deposited: 29 Nov 2022 04:39
Last Modified: 23 Dec 2023 05:24
URI: http://go7publish.com/id/eprint/524

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