Semantic Segmentation of Coffee Regions From Satellite Images Using Deep Learning

Coffee is one of the main agricultural products exported by Brazil, with Minas Gerais standing out as the largest producer and responsible for a significant portion of the country's production. Due to its importance, this work investigates various Convolutional Neural Networks (CNNs) for mapping coffee plantation areas in the municipality of Muzambinho, MG. Using satellite images, we evaluated four semantic segmentation network architectures: FCN, MA-NET, SegNet, and DeepLabV3. Experiments using a dataset with 100 satellite images demonstrated that the FCN and MA-NET networks achieve better performance in identifying crops than SegNet and DeepLabV3. The latter, however, presented generalization challenges in the test set, indicating sensitivity to variations in lighting and shading. The study highlights the potential of artificial intelligence combined with remote sensing to improve agricultural management, and the need for more robust and diversified datasets to optimize the generalization and robustness of the models in real conditions.