A Mixture Model for Fruit Ripeness Identification in Deep Learning

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Author(s): Bingjie Xiao (Auckland University of Technology, New Zealand), Minh Nguyen (Auckland University of Technology, New Zealand)and Wei Qi Yan (Auckland University of Technology, New Zealand)
Copyright: 2024
Pages: 21
Source title: Handbook of Research on AI and ML for Intelligent Machines and Systems
Source Author(s)/Editor(s): Brij B. Gupta (Asia University, Taichung, Taiwan & Lebanese American University, Beirut, Lebanon)and Francesco Colace (University of Salerno, Italy)
DOI: 10.4018/978-1-6684-9999-3.ch016

Keywords: Artificial Intelligence / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Visual object detection is a foundation in the field of computer vision. Since the size of visual objects in an images is various, the speed and accuracy of object detection are the focus of current research projects in computer vision. In this book chapter, the datasets consist of fruit images with various maturity. Different types of fruit are divided into the classes “ripe” and “overripe” according to the degree of skin folds. Then the object detection model is employed to automatically classify different ripeness of fruits. A family of YOLO models are representative algorithms for visual object detection. The authors make use of ConvNeXt and YOLOv7, which belong to the CNN network, to locate and detect fruits, respectively. YOLOv7 employs the bag-of-freebies training method to achieve its objectives, which reduces training costs and enhances detection accuracy. An extended E-ELAN module, based on the original ELAN, is proposed within YOLOv7 to increase group convolution and improve visual feature extraction. In contrast, ConvNeXt makes use of a standard neural network architecture, with ResNet-50 serving as the baseline. The authors compare the proposed models, which result in an optimal classification model with best precision of 98.9%.

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A Mixture Model for Fruit Ripeness Identification in Deep Learning

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