: In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthyobjects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization(PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed modelincludes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim,by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification,adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images inthe preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of theimages, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined bymeans of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded,and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determinedthreshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. Inorder to illustrate the classification success of our approaches, the same classification experiments were reimplementedby directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimentalresults showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.