A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Abstract

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects 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 model includes 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 from 200 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 in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means 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 determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results 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.

Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması

“…Veri kümesine ait kapsamlı özellikleri. Derin öğrenme yaklaşımları, verilerin etiketli ve etiketsiz olma duruma göre sınıflandırılır [28]- [34]. Verinin etiketli bir formu varsa o zaman denetimli öğrenme, etiketsiz bir formu olursa o zaman ise denetimsiz öğrenme ve son olarak ise hem etiketli hem de etiketsiz veri kullanılıyorsa yarı denetimli öğrenme diye sınıflandırılır.…”

Early Bruise Detection in Apple Based on an Improved Faster RCNN Model

“…In recent years, many imaging techniques have been applied to the non-destructive detection of bruises in fruits and vegetables, such as nuclear magnetic resonance (NMR) [10], X-ray imaging technology [11][12][13], and hyperspectral techniques [14][15][16][17][18]. However, the NMR has not been widely used due to its high cost.…”

“…The research mentioned above primarily focused on studying the equatorial surface of apples, without considering the interference caused by the apple's calyx and stem in the actual production process for detection [18]. This may lead to a decrease in the accuracy of bruise detection in apples.…”

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

“…These methods have become important in numerous research areas, such as image processing and data classi cation. As a result, machine learning and deep learning methods have been applied in various elds, including medicine, industrial applications, energy systems, and agriculture [3,4,5]. Farmers can use machine learning and deep learning-based applications to monitor crop production processes in natural and greenhouse environments.…”