Melon classification using convolutional neural network models
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Author |
Rex M. Romero, Adriel Joshua Cabal, Ashlee Vance Oczon, Mark Jonelle Operiano, Jomer V. Catipon and Jessica S. Velasco
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e-ISSN |
1819-6608 |
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On Pages
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769-778
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Volume No. |
20
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Issue No. |
11
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Issue Date |
August 31, 2025
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DOI |
https://doi.org/10.59018/062592
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Keywords |
image classification, sorting, computer vision, melon, deep learning.
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Abstract
This paper discusses the need for more accurate and efficient technologies for automating melon classification in the agricultural sector. Deep learning, specifically convolutional neural networks and recurrent neural networks, is a promising method for achieving this. The study assesses the accuracy, speed, and scalability of several deep learning models and investigates integrating other technologies like IoT devices and remote sensing to enhance capabilities. Environmental impact and ethical considerations are also addressed. The summary of different deep learning models, along with their file sizes and Input Image, are studied. In conclusion, the smaller models, such as DenseNet121, have lower weights (29.5MB); on the other hand, the larger models, such as InceptionV3 and Xception, have larger file sizes (87.0MB and 83.0MB, respectively). When selecting models based on storage and computational limitations, it is essential to consider these metrics. The size of data and the hardware used, such as a GPU or CPU, can be changed. Distinct characteristics emerge when comparing various models based on weight size, loading time, and accuracy. The conclusion is DenseNet201 model stands out with the highest accuracy at 99.14%, but it also entails the lengthiest loading time (1.19 hours), albeit having a relatively smaller weight size of 74.6MB. In contrast, InceptionV3 demonstrates a respectable accuracy of 96.45% with a shorter loading time (21.53 minutes), despite a larger weight size of 87.00MB. On the other hand, VGG19, despite having the lowest accuracy at 80.20%, has a weight size of 77.21MB and the longest loading time (1.17 hours).
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