Deep Neural Networks For Precision Agriculture: Automatic Classification Of Farmland From Satellite Imagery

Abstract

The integration of deep learning into precision agriculture has transformed land monitoring by enabling automated, accurate mapping of farmland from satellite imagery. In this study, we develop and evaluate convolutional neural network (CNN) models for semantic segmentation of farmland areas, utilizing multispectral data from Sentinel-2 and Landsat-8. We describe a pipeline combining data preparation, deep convolutional networks, and post-processing to classify each image pixel into farmland vs. non-farmland (and other classes). We experiment with state-of-the-art architectures including U-Net, ResNet-based models, and attention-enhanced CNNs. Our experiments use public datasets (e.g. CORINE, DeepGlobe) and achieve high performance: the best models reach over 90% overall accuracy (OA) and F1-scores above 0.90, outperforming traditional machine learning baselines. For instance, a transfer-learning ResUNet achieved an IoU of 0.81 on DeepGlobe data. Comparative results are reported in tables. Figures illustrate model architectures and example segmentation results. This work demonstrates that modern deep networks can robustly extract farmland from remote sensing data, supporting precision agriculture applications such as crop monitoring and land-use planning.