Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
This work addresses the problem of costly and time-consuming ground-based surveys for policymakers assessing agricultural practices, though it is incremental as it applies existing methods to a new domain.
The study tackled the challenge of monitoring weed management methods in orchards by developing separate machine learning models using Sentinel-2 and PlanetScope satellite data to classify four methods, demonstrating the potential of ML-driven remote sensing to improve mapping efficiency and accuracy.
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.