Starch plays a pivotal role in human society, serving as a vital component of our food sources and finding widespread applications in various industries. Microscopic imaging offers a straightforward, efficient, and precise approach to examine the distribution, morphology, and dimensions of starch granules. Quantitative analysis through the segmentation of starch granules from the background aids researchers in exploring their physicochemical properties. This article presents a novel approach utilizing a modified U-Net model in deep learning to achieve the segmentation of starch granule microscope images with remarkable accuracy. The method yields impressive results, with mean values for several evaluation metrics including JS, Dice, Accuracy, Precision, Sensitivity and Specificityreaching 89.67%, 94.55%, 99.40%, 94.89%, 94.23% and 99.70%, respectively.