MSA-Net: Potato Pollen Image Segmentation Based on the U-Net Model with Multi-Scale Attention Mechanism

Potato pollen segmentation is a pivotal task in agricultural image analysis, underpinning critical applications in plant breeding, disease monitoring, and genetic diversity assessment. Conventional methods for pollen viability detection via traditional image processing often encounter bottlenecks in efficiency and accuracy, limiting their utility in high-throughput scenarios. Addressing these challenges, this paper introduces a novel multi-scale attention U-Net architecture tailored for precise potato pollen segmentation and viability assessment. Our methodology leverages a rigorously annotated dataset of 45 highresolution microscopic images (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3600 \times 3600$</tex> pixels), capturing diverse pollen morphologies under standardized conditions. At the core of this method lies a distinctive symmetric architecture complemented by skip connections, which enables the seamless integration of contextual and detailed information. This design effectively captures both the broad semantic context and finegrained details, laying a solid foundation for precise segmentation. Furthermore, multi-scale dilated convolutions are strategically employed to dynamically extract hierarchical features across diverse scales, adeptly adapting to the morphological variability of pollen grains. Simultaneously, a channel attention mechanism is incorporated to selectively emphasize crucial information channels, significantly enhancing the discriminative power of the model. These innovative components work in synergy to elevate the accuracy of segmentation results, thereby enabling highly efficient pixel-level image segmentation. Experimental results demonstrate the model's superior performance, achieving an accuracy of 99.15% in pixel-wise segmentation a significant improvement over existing methods.