With the development of agricultural modernization, strengthening the supervision of agricultural production activities plays a crucial role in social security management and economic development. However, the current monitoring of greenhouse usage and planning in agricultural production lacks effective regulation. Existing technological approaches involve the analysis and monitoring of agricultural activities using machine learning and simple neural networks, but their detection accuracy is limited. In this paper, we present a novel deep learning model called FADNet, which fusion attention mechanism and variable convolution techniques. FADNet utilizes remotely sensed images obtained from satellite sensors as input for training. It employs variable convolution and feature pyramid networks to achieve accurate segmentation of small-scale greenhouse targets. Spatial attention mechanism is employed to address the interference caused by similar features in urban areas for greenhouse identification. Additionally, data augmentation techniques are utilized to address the scarcity of greenhouse datasets and the disparity in the distribution of positive and negative samples, thereby enhancing the reliability of the dataset. FADNet achieves greenhouse segmentation by performing pixel-level classification on the images. Extensive experiments have demonstrated that FADNet performs exceptionally well in greenhouse segmentation tasks.