Carrot is one of the agricultural commodities with high nutritional value and a significant market demand. However, its quality can deteriorate due to various factors, one of which is rotting. Early detection of rotting carrots is crucial to prevent economic losses and maintain product quality. The main problem in identifying rotten carrots lies in the need for high precision and the time-consuming nature of manual methods. To address this issue, this research develops an automated method for detecting rotten carrots using image processing techniques. In this study, edge detection and convolution techniques are employed as the primary approaches in image analysis. Edge detection is used to recognize contours and boundaries in carrot images, while convolution techniques are applied to identify patterns of damage and texture differences between rotten and healthy carrots. The research findings indicate that this method is capable of detecting rotten carrots with high accuracy, making it reliable as a tool for sorting and quality assurance in carrot processing.