Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network

Abstract

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.

A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit

“…Generally, it spans an average duration of 31 to 41 days, roughly equivalent to one and a half months, for the fruit to attain its full mature size [1] . Furthermore, it is important to harvest the fruit at its optimal stage of maturity to ensure the best quality, flavor, and texture [11] . Table 2 explains each category in both the Dragon Fruit Maturity Detection and Quality Grading Dataset.…”

Điều khiển hai biến nhiệt độ và độ ẩm đồng bộ sử dụng bộ điều khiển mờ trong nhà màng trồng ớt chuông

“…Hệ thống tưới nước có thể được kết hợp với phân bón hoặc chất dinh dưỡng để cung cấp các chất cần thiết cho cây trồng. Với sự phát triển của khoa học và công nghệ tại Việt Nam, nhiều hệ thống tự động được ứng dụng cho nhiều lĩnh vực khác nhau đặc biệt là trong nông nghiệp [3,4]. Để có thể xuất khẩu và nâng cao chất lượng sản phẩm nông sản các công nghệ cần được ứng dụng vào các hệ thống canh tác nông nghiệp, đây được xem là thách thức đối với ngành nông nghiệp hiện này [5].…”

Development of a Mango-Grading and -Sorting System Based on External Features, Using Machine Learning Algorithms

“…In the other study in [28], the eight-layer CNN focused on general fruit classification and obtained an accuracy of 95.67%. Meanwhile, the fusion of image processing and ANN in [29] for dragon fruit classification resulted in an accuracy of 83.10%, and the combination of KNN and CNN in [29] for dragon fruit achieved 92.85%. Notably, the proposed method for mango-quality classification outperformed all other models, achieving the highest accuracy, of 98.63%.…”