Tuta absoluta is a major threat to tomato production, causinglosses ranging from 80% to 100% when not properly managed.Early detection of T. absoluta’s effects on tomato plants isimportant in controlling and preventing severe pest damageon tomatoes. In this study, we propose semantic and instancesegmentation models based on U-Net and Mask RCNN, deepConvolutional Neural Networks (CNN) to segment the effects ofT. absoluta on tomato leaf images at pixel level using field data.The results show that Mask RCNN achieved a mean AveragePrecision of 85.67%, while the U-Net model achieved anIntersection over Union of 78.60% and Dice coefficient of82.86%. Both models can precisely generate segmentationsindicating the exact spots/areas infested by T. absoluta intomato leaves. The model will help farmers and extension officersmake informed decisions to improve tomato productivityand rescue farmers from annual losses.