用卫星影像+机器学习，自动识别果园四种除草方式。
Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
- 分别用哨兵2号和行星数据构建分类模型
- 四类除草方法识别准确率显著提升
- 适合农业监管与可持续管理决策
有效的杂草管理对提高农业生产力至关重要，因为杂草会与作物争夺养分和水分等关键资源。准确绘制杂草管理方法地图，有助于政策制定者评估农民实践、评估对植被健康、生物多样性和气候的影响，并确保政策合规与补贴发放。然而，监测杂草管理方法通常依赖地面调查，成本高、耗时长且易延迟。为此，本文利用遥感数据与机器学习技术，分别基于哨兵2号（Sentinel-2）和行星数据（PlanetScope）的时序卫星影像，构建独立的机器学习模型，对果园中四种典型杂草管理方式（割草、耕作、化学喷洒、无措施）进行分类。结果表明，基于机器学习的遥感方法在提升果园杂草管理制图的效率与准确性方面具有巨大潜力。
原文摘要 · Abstract (English)
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.
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