This article focuses on heartwood segmentation from cross-section RGB images (see Fig.1). In this context, we propose a novel attention gate (AG) model for both improving performance and making light convolutional neural networks (CNNs). Our proposed AG is based on mathematical morphology operators. Our light CNN is based on the U-Net architecture and called Light U-net (LU-Net). Experimental results show that AGs consistently improve the prediction performance of LU-Net across different wood cross-section datasets. Our proposed morphological AG achieves better performance than original U-Net with 10 times less parameters.