Analyzing Different Architectures of Convolutional Neural Networks for Tomato Grading System
S M Abdullah Al Shuaeb, Anwar Hossen, Md Ashikul Haque Dinar, Utpal Kanti Roy
Asian Journal of Research in Computer Science · pp. 137–147 · Published 14 Dec 2024
10.9734/ajrcos/2024/v17i12534Abstract
The tomato is a very popular and commonly eaten fruit. Its quality, which affects how people see it, depends a lot on how it looks. Convolutional neural networks, which are advanced computer programs, are great at using deep learning to sort and classify fruits, grains, and vegetables in farming. Right now, there are two ways to classify tomatoes: by eye or by analyzing images. The first method, which involves checking tomatoes by hand, is more accurate but takes longer and costs more. The second method, which uses images, is faster and cheaper but not as precise. In this study, we use a deep learning approach to classify tomato quality, specifically using convolutional neural networks (CNNs). We compared two popular CNN models, ResNet-50 and AlexNet, and tested how well these models automatically find important features in the tomatoes. The success percentage of our suggested strategy in experiments was 99.1%. Our proposed method outperforms existing image-processed tomato quality rating systems on all five of the commonly used evaluation criteria, including accuracy, precision, recall, specificity, and F-score.
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Sangeeta Saini, Ashok Kumar, Neeraj Kumari · 2025
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