Design and evaluation of an intelligent sorting system for bell pepper using deep convolutional neural networks

Abstract

Homogeneity of appearance attributes of bell peppers is essential for consumers and food industries. This research aimed to develop an in-line sorting system using a deep convolutional neural network (DCNN) which is considered the state-of-the-art in the field of machine vision-based classifications, for grading bell peppers into five classes. According to export standards, the crop should be graded based on maturity stage and size. For that, the fully connected layer in the ResNet50 architecture of DCNN was replaced with a developed classifier block, including a global average-pooling layer, dense layers, batch normalization, and dropout layer. The developed model was trained and evaluated through the five-fold cross-validation method. The required processing time to classify each sample in the proposed model was estimated as 4 ms which is fast enough for real-time applications. Accordingly, the DCNN model was integrated with a machine vision-based designed sorting machine. Then, the developed system was evaluated in the in-line phase. The performance parameters in the in-line phase include accuracy, precision, sensitivity, specificity, F1-score, and overall accuracies were 98.7%, 97%, 96.9%, 99%, 96.9%, and 96.9%, respectively. The total rate of sorting the bell pepper was also measured as approximately 3000 sample/h with one sorting line. The proposed sorting system demonstrates a very good capability that allows it to be used in industrial applications.

License Plate Recognition System Based on Improved YOLOv5 and GRU

“…The sub-images (4,5,8) show the license plate recognition under different lighting scenes, and the experimental results show that the license plate recognition is accurate with recognition precision of 97.2%, 96.2% and 97.9%, respectively. Sub-images (1,3,6,7) show the license plate recognition under complex character conditions, which include consecutive identical characters, numbers and letters with similar shapes and Chinese abbreviations of different provinces in the license plate, and the experimental results show that the model has a good recognition effect. As shown above, the model proposed in this paper can accurately recognize license plate images in various scenes with strong stability and robustness.…”

“…scenarios. In recent years, with the rapid development of computer hardware, neural network models based on deep learning have become the best tools to solve complex computer vision problems [1], [2], [3]. Convolutional Neural Network(CNN) is one of the best deep learning techniques for target detection and recognition tasks, and the most popular algorithm in CNN-based target detection is YOLO, proposed by Redmon in 2015 [4].…”

Recent Advance of Intelligent Packaging Aided by Artificial Intelligence for Monitoring Food Freshness

“…Khaled [96] this developed an online classification system using deep convolutional neural networks (DCNN) for classifying bell peppers into five categories. According to export standards, crops are graded according to maturity stage and size, and then food grade bell peppers are retained, saving time while reducing manpower.…”

Seasonal Difference of Fruit Quality Attributes and Physiological Disorders in Paprika Cultivars under a Simulated Export System

“…Immediately after harvest, paprika fruit are sorted using a fruit quality grading system that takes into account the pericarp color coverage and fruit size (or fruit weight) (Fox et al, 2005;Díaz-Pérez et al, 2007;Lim et al, 2007;Mohi-Alden et al, 2022). The fruit are packed into suitable packaging materials and then directly distributed into wholesale and retail markets or shipped for export (Singh et al, 2014;Seo et al, 2019;.…”