Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as they commonly rely on ground-based field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage earth observation data and Machine Learning (ML). Specifically, we developed separate ML models using Sentinel-2 and PlanetScope satellite time series data, respectively, to classify four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards. The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.
@article{arxiv.2504.19991,
title = {Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data},
author = {Ioannis Kontogiorgakis and Iason Tsardanidis and Dimitrios Bormpoudakis and Ilias Tsoumas and Dimitra A. Loka and Christos Noulas and Alexandros Tsitouras and Charalampos Kontoes},
journal= {arXiv preprint arXiv:2504.19991},
year = {2025}
}
Comments
Accepted for 2025 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2025)