Classification of Kinalabasa Tomato Using Convolutional Neural Network

Authors

  • Jethel Mae G. Nalupano College of Computer Studies, University of Antique, Sibalom, Antique, Western Visayas, Philippines
  • Myline P. Omagap College of Computer Studies, University of Antique, Sibalom, Antique, Western Visayas, Philippines
  • Katherine Jane G. Fortaleza College of Computer Studies, University of Antique, Sibalom, Antique, Western Visayas, Philippines
  • Fema Rose B. Ecraela College of Computer Studies, University of Antique, Sibalom, Antique, Western Visayas, Philippines
  • John Jowil D. Orquia College of Computer Studies, University of Antique, Sibalom, Antique, Western Visayas, Philippines

DOI:

https://doi.org/10.69478/JITC2024v6n3a10

Keywords:

Machine Vision, Convolutional Neural Network, Native Tomato Classification, Machine Learning

Abstract

Determining the freshness of tomatoes is essential for evaluating their quality, impacting both consumer satisfaction and the economic benefits for farmers. Freshness is typically assessed by outer appearance, including color, size, and shape, with skin color indicating ripeness and influencing selling price. Properly assessing freshness also helps determine the shelf life of stored tomatoes. This study introduced an affordable and straightforward technique for classifying agricultural commodities using image processing, focusing on Kinalabasa tomatoes and classifying them based on color features into three categories. The study employed the MobileNetv2 architecture for training and testing the dataset, aiming to improve the classification of Kinalabasa tomatoes through deep learning techniques. The study evaluated the model's performance using metrics like accuracy, precision, sensitivity, and specificity. The optimal configuration for classifying Kinalabasa tomatoes was determined to be 10 epochs and a learning rate of 0.001. MobileNetv2 achieved a high accuracy rate of 94%, demonstrating its effectiveness in classifying tomatoes, though the specificity rate of 64% indicated some room for improvement in identifying negative instances. Comparing MobileNetv2 with ShuffleNet architectures will provide further insights into their respective effectiveness and performance in this classification task.

References

E. O. Brown, R. V. Ebora, and F. L. C. Decena, “The Current State, Challenges and Plans for Philippine Agriculture,” FFTC Agricultural Policy Platform (FFTC-AP), November 2018, https://ap.fftc.org.tw/article/500.

D. Bhowmik, K. P. S. Kumar, S. Paswan, and S. Srivastava, “Tomato - A Natural Medicine and Its Health Benefits,” Journal of Pharmacognosy and Phytochemistry, vol. 1, no. 1, May 2012, pp. 33-43, ISSN 2278-4136.

W. Klunklin and G. Savage, “Effect on Quality Characteristics of Tomatoes Grown Under Well-Watered and Drought Stress Conditions,” Foods, vol. 6, no. 8, August 2017, https://doi.org/10.3390/foods6080056.

M. B. Deshetti, M. Y. Teggi, and A. Durgad, “Growth and Export Performance of Tomato in India,” International Journal of Economic and Business Review, vol. 3, no. 1, January 2015, pp. 48-52, e-ISSN: 2347-9671.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, May 2017, pp. 84-90, https://doi.org/10.1145/306538.

M. Brahimi, M. Arsenovic, S. Laraba, S. Sladojevic, K. Boukhalfa, and A. Moussaoui, “Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation,” in Human and Machine Learning. Human–Computer Interaction Series, J. Zhou, F. Chen (Eds.), Springer International Publishing: Cham, Switzerland, June 2018, pp. 93-117, https://doi.org/10.1007/978-3-319-90403-0_6.

K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, February 2018, pp. 311-318, https://doi.org/10.1016/j.compag.2018.01.009.

C. Thomas, “An introduction to Convolutional Neural Networks,” Medium, May 2019, https://towardsdatascience.com/an-introduction-to-convolutional-neural-networks-eb0b60b58fd7.

S. Saha, “A Comprehensive Guide to Convolutional Neural Networks – the ELI5 Way,” Medium, December 2018, https://saturncloud.io/blog/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way/.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional Neural Networks: An Overview and Application in Radiology,” Insights into Imaging,” vol. 9, June 2018, pp. 611-629, https://doi.org/10.1007/s13244-018-0639-9.

M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, M. Hasan, B. C. Van Essen, A. A. S. Awwal, V. K. Asari, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” Electronics, vol. 8, no. 3, March 2019, pp. 292, https://doi.org/10.3390/electronics8030292.

M. Yawar, “MobileNet,” Code Studio, March 2024, https://www.naukri.com/code360/library/mobilenet.

V. Bhole and A. Kumar, “A Transfer Learning-based Approach to Predict the Shelf life of Fruit,” Inteligencia Artificial, vol. 24, no. 67, June 2021, pp. 102-120, https://doi.org/10.4114/intartif.vol24iss67pp102-120.

P. Kasture and K. Shirsath, “Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis,” Indian Journal of Science and Technology, vol. 17, no. 18, April 2024, pp. 1880-1888, https://doi.org/10.17485/IJST/v17i18.466.

S. S. Pandi, V. R. Chiranjeevi, T. Kumaragurubaran, and P. Kumar, “Improvement of Classification Accuracy in Machine Learning Algorithm by Hyper-Parameter Optimization,” in Proc. 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India, November 1-2, 2023, pp. 1-5, https://doi.org/10.1109/RMKMATE59243.2023.10369177.

J. Brownlee, “Difference Between a Batch and an Epoch in a Neural Network,” Machine Learning Mastery, August 2022, https://machinelearningmastery.com/difference-between-a-batch-and-an-epoch/.

S. J. Bell, O. P. Kampman, J. Dodge, and N. D. Lawrence, “Modeling the Machine Learning Multiverse,” in Proc. of the 36th International Conference on Neural Information Processing System, June 2022, pp. 18416-18429, https://doi.org/10.48550/arXiv.2206.05985.

D. Bertsimas, D. Zhuo, J. Dunn, J. Levine, E. Zuccarelli, N. Smyrnakis, Z. Tobota, B. Maruszewski, J. Fragata, G. E. Sarris, “Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach,” World Journal for Pediatric and Congenital Heart Surgery, vol. 12, no. 4, July 2021, pp. 453-460, https://doi.org/10.1177/21501351211007106.

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Published

2024-09-30

How to Cite

Classification of Kinalabasa Tomato Using Convolutional Neural Network. (2024). Journal of Innovative Technology Convergence, 6(3). https://doi.org/10.69478/JITC2024v6n3a10

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