Abstract
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits' recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits' production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements.
Highlights
We are in an era where we still use bar code technology in fruit shops and supermarkets to get fruit prices and to get other information such as source traceback. is is a big challenge for shopkeepers to remember and manage the bar codes for individual fruit categories
We proposed a simple and efficient fruit and vegetable detection and classification algorithm using a deep convolutional neural network. e main aim of this paper is to apply deep learning with the data expansion techniques to 20 different categories of fruits and vegetables
As we know that choosing a convolutional neural network (CNN) architecture for real-time object identification and recognition is a tough undertaking because the exact number of layers, kind of layers, and the number of neurons to utilize in each layer are all difficult to determine, in this paper, we have examined different network architectures to find the best one
Summary
We are in an era where we still use bar code technology in fruit shops and supermarkets to get fruit prices and to get other information such as source traceback. is is a big challenge for shopkeepers to remember and manage the bar codes for individual fruit categories. We are in an era where we still use bar code technology in fruit shops and supermarkets to get fruit prices and to get other information such as source traceback. Is is a big challenge for shopkeepers to remember and manage the bar codes for individual fruit categories. Fruit shops and supermarkets pack fruit and vegetables inside the small boxes and use bar codes to determine their prices. E convolutional neural network (CNN) is a neural network that can be used to enable machines to visualize things and perform a task such as an image classification and recognition [3]. E CNN can take input images, process them, and classify certain classifications. Image processing uses CNNs as one of the most common deep learning techniques [4]. Nuske et al [5] proposed a visual grape
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