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This study introduces a scalable remote sensing approach to detect cashew orchards across Guinea-Bissau using Sentinel-2 satellite imagery and machine learning techniques. By employing margin-based active learning, the authors optimized the training dataset for maximum informativeness, achieving a remarkable 94.0% balanced accuracy in orchard detection. The resulting 2021 cashew map, available at 10m spatial resolution, addresses the lack of a nationwide database and provides critical insights for environmental management and economic planning.
Achieving 94% accuracy in nationwide cashew orchard detection using satellite imagery could revolutionize environmental monitoring and agricultural management in Guinea-Bissau.
Cashew production is a widespread economic activity in Guinea-Bissau, as well as other countries in West Africa. However, unregulated cashew production can be directly associated with increasing regionwide deforestation rates, biodiversity losses, and a fragile economic structure. There is no nationwide database for listing or georeferencing cashew orchards, so there is a clear need to remotely map their locations. In recent years, multiple methods for detecting orchards have been developed, though they have only been applied on a regional level. This work expands regional analyses to a nationwide scale. It develops a scalable and cost-effective remote approach, based on Sentinel-2 satellite imagery, using Machine Learning techniques to detect cashew orchards automatically. Margin-based Active Learning techniques were employed to develop an optimal training set in terms of the number of points and their informativeness, leading to a cashew map with 94.0% balanced accuracy obtained entirely off-site. We created two datasets and a 2021 cashew map with 10m spatial resolution that are openly accessible through GitHub. The results demonstrate the possibility of a broader cashew orchard mapping, creating a new stepping stone for this environmental application.
