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Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images

Andreas Tritsarolis, Tomaž Bokan, Matej Brumen, Domen Mongus, Yannis Theodoridis

TL;DR

TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels is presented and an experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.

Abstract

The modernization of agriculture has motivated the development of advanced analytics and decision-support systems to improve resource utilization and reduce environmental impacts. Targeted Spraying and Fertilization (TSF) is a critical operation that enables farmers to apply inputs more precisely, optimizing resource use and promoting environmental sustainability. However, accurate TSF is a challenging problem, due to external factors such as crop type, fertilization phase, soil conditions, and weather dynamics. In this paper, we present TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels. Our experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.

Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images

TL;DR

TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels is presented and an experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.

Abstract

The modernization of agriculture has motivated the development of advanced analytics and decision-support systems to improve resource utilization and reduce environmental impacts. Targeted Spraying and Fertilization (TSF) is a critical operation that enables farmers to apply inputs more precisely, optimizing resource use and promoting environmental sustainability. However, accurate TSF is a challenging problem, due to external factors such as crop type, fertilization phase, soil conditions, and weather dynamics. In this paper, we present TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels. Our experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.
Paper Structure (10 sections, 3 equations, 2 figures, 1 table)

This paper contains 10 sections, 3 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation and Proposed Methodology
  4. Problem Definition
  5. Methodology
  6. Experimental Study
  7. Experimental Setup, Datasets and Preprocessing
  8. Experimental Results on TerrAI
  9. A Note on the Energy Consumption and CO2 Emission Savings of TerrAI
  10. Conclusion

Figures (2)

  • Figure 1: Architecture overview of the proposed TerrAI framework.
  • Figure 2: Estimation of the prescription map for two randomly selected test parcels from the ITC dataset using TerrAI. Left: input soil-health tensor (one spectral image); middle and right: actual and estimated prescription maps.

Theorems & Definitions (3)

  • Definition 1: Soil-health
  • Definition 2: Prescription map
  • Definition 3: Targeted Spraying and Fertilization - TSF