This paper tries to combat the food waste of strawberries during the harvesting steps. An automatic pipeline must be established to combat this food waste. One of the steps needed in this pipeline is detecting strawberries in images. Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to detect strawberries. Faster r-cnn, Mask r-cnn and RetinaNet are compared against each other using different setting. Mask r-cnn achieved the highest average bounding box and segmentation mAP with 51.63 and 73.20 respectively.