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

TL;DR

This paper introduces TerrAI, a neural network model that improves targeted spraying and fertilization in wheat fields by accurately estimating nitrogen levels from multispectral images, considering spatial and temporal variability.

Contribution

The paper presents a novel neural network approach, TerrAI, for data-driven nitrogen estimation in wheat fields using multispectral imagery, addressing variability factors.

Findings

TerrAI outperforms baseline models in nitrogen estimation accuracy.

The model effectively captures spatio-temporal variability in agricultural data.

Experimental validation confirms its potential for real-world precision agriculture.

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.