Tomato Ripeness Detection and Classification using VGG based CNN Models
Keywords:
Computer Vision (CV), Deep Learning (DL), Transfer Learning (TL), Multi-Layer Perceptron.Abstract
Ripening is a normal phase in the maturation process of fruits and vegetables. Computer Vision (CV) along with deep learning models provided several opportunities in the field of the agriculture. One of the important applications of CV is to detect and identify the ripeness of the fruits and vegetables. Also, accurately detecting ripening of vegetables using computer vision aids the farmers in better harvesting. For this purpose, deep learning models are used to extracts in-depth features from the images that consumes less time compared to traditional methods. These deep learning approaches, on the other hand, necessitate a big dataset and longer time for image classification. Many studies recommended using the transfer learning method to solve these issues. This paper proposes a model for the tomato ripeness detection and classification using transfer learning which uses VGG16 model. Further, to improve the efficiency of the method the top layer is replaced by a as Multi-Layer Perceptron (MLP) and employed a strategy of fine-tuning approach. The proposed model with fine-tuning approach gives a better efficacy on the tomato ripeness detection and classification.
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Sapan Naik and Bankim Patel. Machine Vision based Fruit Classification and Grading - A Review. International Journal of Computer Applications 170(9):22-34, July 2017
Kalia, Parul, Akash Garg, and Amit Kumar. "Fruit quality evaluation using Machine Learning: A review." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), vol. 1, pp. 952-956. IEEE, 2019.
H. U. Rehman and J. Miura, "Viewpoint Planning for Automated Fruit Harvesting Using Deep Learning," 2021 IEEE/SICE International Symposium on System Integration (SII), 2021, pp. 409-414, doi: 10.1109/IEEECONF49454.2021.9382628.
Nguyen, H.H.C., Luong, A.T., Trinh, T.H., Ho, P.H., Meesad, P., Nguyen, T.T. (2021). Intelligent Fruit Recognition System Using Deep Learning. In: Meesad, P., Sodsee, D.S., Jitsakul, W., Tangwannawit, S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_2
H. Muresan and M. Oltean, “Fruit recognition from images using deep learning”, Acta Univ. Sapientiae, Informatica, 2018, pp. 26-42.
Yuesheng, F., Jian, S., Fuxiang, X., Yang, B., Xiang, Z., Peng, G., Zhengtao, W. and Shengqiao, X., 2021. Circular fruit and vegetable classification based on optimized GoogLeNet. IEEE Access, 9, pp.113599-113611.
Saranya, N., Srinivasan, K. and Kumar, S.K., 2021. Banana ripeness stage identification: a deep learning approach. Journal of Ambient Intelligence and Humanized Computing, pp.1-7.
Hakim, L., Kristanto, S.P., Yusuf, D., Shodiq, M.N. and Setiawan, W.A., 2021. Disease detection of dragon fruit stem based on the combined features of color and texture. INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, 5(2), pp.161-175.
Tanco, M.M., Tejera, G. and Di Martino, M., 2018, January. Computer Vision based System for Apple Detection in Crops. In VISIGRAPP (4: VISAPP) (pp. 239-249).
Magsi, A., Mahar, J.A. and Danwar, S.H., 2019. Date fruit recognition using feature extraction techniques and deep convolutional neural network. Indian Journal of Science and Technology, 12(32), pp.1-12.
Wu, S.L., Tung, H.Y. and Hsu, Y.L., 2020, December. Deep learning for automatic quality grading of mangoes: methods and insights. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 446-453). IEEE.
Chen, Y., An, X., Gao, S., Li, S. and Kang, H., 2021. A deep learning-based vision system combining detection and tracking for fast on-line citrus sorting. Frontiers in Plant Science, 12, p.622062.
Clement, Javier, Nuria Novas, José-Antonio Gazquez, and Francisco Manzano-Agugliaro. "High speed intelligent classifier of tomatoes by colour, size and weight." Spanish Journal of Agricultural Research 10, no. 2 (2012): 314-325..
Chakraborty, Sovon, FM Javed Mehedi Shamrat, Md Masum Billah, Md Al Jubair, Md Alauddin, and Rumesh Ranjan. "Implementation of deep learning methods to identify rotten fruits." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1207-1212. IEEE, 2021.
R. Hamza and M. Chtourou, "Apple Ripeness Estimation Using Artificial Neural Network," 2018 International Conference on High Performance Computing & Simulation (HPCS), 2018, pp. 229-234, doi: 10.1109/HPCS.2018.00049.
Worasawate, Denchai, Panarit Sakunasinha, and Surasak Chiangga. "Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach." AgriEngineering 4, no. 1 (2022): 32-47.
Garcia, M. B., Ambat, S., & Adao, R. T. (2019, November). Tomayto, tomahto: A machine learning approach for tomato ripening stage identification using pixel-based color image classification. In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (pp. 1-6). IEEE.
Xiao, Qingmei, Wendi Niu, and Hong Zhang. "Predicting fruit maturity stage dynamically based on fuzzy recognition and color feature." In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 944-948. IEEE, 2015.
Shahid, Shamim Ibne, and Md Shahjahan. "A new approach to image classification by convolutional neural network." In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), pp. 1-5. IEEE, 2017.
Biswas, B., Ghosh, S.K. and Ghosh, A., 2020. A robust multi-label fruit classification based on deep convolution neural network. In Computational Intelligence in Pattern Recognition (pp. 105-115). Springer, Singapore.
T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, “Pcanet: A simple deep learning baseline for image classification?” IEEE transactions on image processing, vol. 24, no. 12, pp. 5017–5032, 2015.
Sambasivam, G. and Opiyo, G.D., 2021. A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Informatics Journal, 22(1), pp.27-34.
KC, K., Li, R. and Gilany, M., 2021. Joint inference for neural network depth and dropout regularization. Advances in Neural Information Processing Systems, 34, pp.26622-26634.
Kim, H.C. and Kang, M.J., 2020. A comparison of methods to reduce overfitting in neural networks. International journal of advanced smart convergence, 9(2), pp.173-178.
Aquino, N.R., Gutoski, M., Hattori, L.T. and Lopes, H.S., 2017. The effect of data augmentation on the performance of convolutional neural networks. Braz. Soc. Comput. Intell.
Wong, S.C., Gatt, A., Stamatescu, V. and McDonnell, M.D., 2016, November. Understanding data augmentation for classification: when to warp?. In 2016 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-6). IEEE.
Ahmad, J., Farman, H. and Jan, Z., 2019. Deep learning methods and applications. In Deep learning: convergence to big data analytics (pp. 31-42). Springer, Singapore.
Rodrigues, B., Kansara, R., Singh, S., Save, D. and Parihar, S., 2021, May. Ripe-Unripe: Machine Learning based Ripeness Classification. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1-5). IEEE.
Zhang, H., Chen, Y., Liu, X., Huang, Y., Zhan, B. and Luo, W., 2021. Identification of common skin defects and classification of early decayed citrus using hyperspectral imaging technique. Food Analytical Methods, 14(6), pp.1176-1193.
Anand, D., Arulselvi, G., Balaji, G. N., & Chandra, G. R. (2022). A Deep Convolutional Extreme Machine Learning Classification Method to Detect Bone Cancer from Histopathological Images. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 39-47.
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