Manuscript Number : IJSRST5241113

Deep Learning-Based Tomato Ripeness Detection : A ResNet-152 Approach

Authors(4) :-Mohammed Mutahar, Shyamalan Kannan, Mohammed Mustafa Jafer, Maneesh Ragavendra K

This research paper presents an advanced deep learning framework for tomato ripeness and maturity detection, employing Convolutional Neural Networks (CNNs) as the primary tool for automated classification. In particular, the study emphasizes the utilization of the ResNet-152 architecture, a sophisticated deep neural network known for its exceptional performance in image classification tasks. ResNet-152 addresses the challenges of fine-grained categorization by enabling the extraction of intricate visual features such as color nuances, textures, and shapes inherent to tomatoes. This eliminates the necessity for manual feature engineering, enhancing the model's ability to discern between "RIPE," "UNRIPE," and "ROTTEN" tomato classes with unprecedented accuracy. The ResNet-152 architecture's effectiveness lies in its unique design, featuring residual blocks that facilitate the training of very deep networks. This architecture mitigates the vanishing gradient problem and enables the model to learn complex hierarchical features, contributing to its state-of-the-art performance in image classification. In the context of tomato ripeness detection, ResNet-152 acts as a powerful tool for capturing and understanding the intricate visual cues that define different ripeness states, laying the foundation for an efficient and accurate automated sorting system in the agricultural and food processing industries.

Authors and Affiliations

Mohammed Mutahar
Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
Shyamalan Kannan
Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
Mohammed Mustafa Jafer
Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
Maneesh Ragavendra K
Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India

Convolutional Neural Networks (CNNs), ResNet-152, deep neural network, fine-grained categorization, residual blocks, tomato ripeness detection, image classification, automated sorting system, agricultural, food processing industries.

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Publication Details

Published in : Volume 11 | Issue 1 | January-February 2024
Date of Publication : 2024-02-29
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 34-41
Manuscript Number : IJSRST5241113
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Mohammed Mutahar, Shyamalan Kannan, Mohammed Mustafa Jafer, Maneesh Ragavendra K, " Deep Learning-Based Tomato Ripeness Detection : A ResNet-152 Approach", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 11, Issue 1, pp.34-41, January-February-2024. Available at doi : https://doi.org/10.32628/IJSRST5241113    
Journal URL : https://ijsrst.com/IJSRST5241113
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