xACO_1DCNN: Ant Colony Optimization Based 1D-Convolution Neural Network based Fruit grading and Classification
Keywords:
fruit classification, mango, feature extraction, ACO, Convolution Neural Network, Segmentation, deep learningAbstract
The automation of fruit classification represents a compelling application of computer vision. Historically, fruit classification methods have frequently depended on manual procedures that rely on human visual perception. However, these methods are characterized by their tedious nature, lengthy time requirements, and lack of consistency.The primary criterion for classifying fruits is their external shape and appearance.In recent years, the fruit industry has witnessed a growing utilization of computer machine vision and image processing techniques. These technologies have proven to be particularly valuable for various applications within the industry, such as quality inspection and the sorting of fruits based on colour, size, and shape. Research conducted in this field suggest that the utilisation of machine vision systems holds promise in enhancing product quality and alleviating the need for manual fruit sorting processes.This paper presents the design and development of an intelligent automation system for the grading of mango fruit.The fruit segmentation process is initially conducted using the U-NET model, which incorporates feature extraction from EffecientNet+ResNet101. The utilisation of the Artificial Bee Colony Optimisation algorithm has been employed in order tominimise and optimise the feature vector.The categorization of fruit quality has been accomplished by considering surface defects and maturity classification. Therefore, in order to classify defects, the 1D-CNN has been trained using optimal segmented features of abnormalities. To enhance the performance, the proposed ACO_1DCNN model is employed to optimise the classification accuracy. The proposed classifier is utilised to categorise the maturity levels as ripe, partially ripe, and unripe.Ultimately, the defect and maturity output have been employed as determinants for categorising the quality as either good, average, or bad. The experimental results demonstrate that the ACO_1DCNN model attains an accuracy of 97.95%, precision of 95.97%, recall of 95.59%, F1-score of 95.76%, AUC score of 99.42%, and MCC of 94.34% when evaluated on the testing dataset.During the training process, the model attains an accuracy of 97.62%, precision of 95.83%, recall of 94.96%, F1-score of 95.33%, AUC score of 99.56%, and MCC of 93.69%.
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D. Pem and R. Jeewon, “Fruit and vegetable intake: benefits and progress of nutrition education interventions- narrative review article,” Iranian Journal of Public Health, vol. 44, no. 10, pp. 1309–1321, 2015.
R. C. Fierascu, E. Sieniawska, A. Ortan, I. Fierascu, and J. Xiao, “Fruits by-products - a source of valuable active principles. a short review,” Frontiers in Bioengineering and Biotechnology, vol. 8, pp. 319–328, 2020.
S. R. Dubey and A. S. Jalal, “Species and variety detection of fruits and vegetables from images,” International Journal of Applied Pattern Recognition, vol. 1, pp. 108–126, 2013.
R. A. A. Al-Fallujah, “Color, shape, and texture based fruit recognition system,” International Journal of Advanced Research in Computer Engineering & Technology, vol. 5, pp. 2108–2112, 2016.
S. Arivazhagan, S. Newlin, N. Selva, and G. Lakshmanan, “Fruit recognition using color and texture features,” Journal of Emerging Trends in Computing and Information Sciences, vol. 1, no. 2, pp. 90–94, 2010.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, et al., "Backpropagation applied to handwritten zip code recognition," Neural computation, vol. 1, pp. 541-551, 1989.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, "Large-scale video classification with convolutional neural networks," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2014, pp. 1725-1732.
Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," in Advances in neural information processing systems, 2014, pp. 1988-1996.
F. Siddique, S. Sakib, and M. A. B. Siddique, "Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers," 2019.
I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez, and C. McCool, "Deepfruits: A fruit detection system using deep neural networks," Sensors, vol. 16, p. 1222, 2016.
D. H. Hubel and T. N. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex," The Journal of physiology, vol. 160, pp. 106-154, 1962.
K. Fukushima and S. Miyake, "Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition," in Competition and cooperation in neural nets, ed: Springer, 1982, pp. 267-285.
C. Hung, J. Nieto, Z. Taylor, J. Underwood, and S. Sukkarieh, "Orchard fruit segmentation using multi-spectral feature learning," in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 5314-5320
Sakib, S., Ashrafi, Z., & Siddique, M. A. B. (2019). Implementation of fruits recognition classifier using convolutional neural network algorithm for observation of accuracies for various hidden layers. arXiv preprint arXiv:1904.00783.
Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., & Zhang, Q. (2020). Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review. Computers and Electronics in Agriculture, 177, 105687.
Yeh, J. F., Lin, K. M., Lin, C. Y., & Kang, J. C. (2023). Intelligent Mango Fruit Grade Classification Using AlexNet-SPP With Mask R-CNN-Based Segmentation Algorithm. IEEE Transactions on AgriFood Electronics.
Kumari, N., Bhatt, A. K., & Dwivedi, R. K. (2023, April). Self-Adaptive Glow Warm Swarm Optimization Technique in Optimal Feature Selection in Grading of Fruit Mango. In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) (pp. 424-430). IEEE.
Rajalaxmi, R. R., Saradha, M., Fathima, S. K., Sathish Kumar, V. E., Sandeep Kumar, M., & Prabhu, J. (2023). An Improved MangoNet Architecture Using Harris Hawks Optimization for Fruit Classification with Uncertainty Estimation. Journal of Uncertain Systems, 16(01), 2242006.
Gururaj, N., Vinod, V., & Vijayakumar, K. (2022). Deep grading of mangoes using Convolutional Neural Network and Computer Vision. Multimedia Tools and Applications, 1-26.
Rizwan Iqbal, H. M., & Hakim, A. (2022). Classification and grading of harvested mangoes using convolutional neural network. International Journal of Fruit Science, 22(1), 95-109.
Kumari, N., Kr. Bhatt, A., Kr. Dwivedi, R., &Belwal, R. (2021). Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer. Multimedia Tools and Applications, 80, 4943-4973.
Shi, R., Li, T., & Yamaguchi, Y. (2020). An attribution-based pruning method for real-time mango detection with YOLO network. Computers and electronics in agriculture, 169, 105214.
Pulfer, E.M. Different Approaches to Blurring Digital Images and Their Effect on Facial Detection; University of Arkansas: Fayetteville, NC, USA, 2019.
J. Teng, S. Wang, J. Zhang, and W. Xue, “Fusion algorithm of medical images based on fuzzy logic,” in 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 546–559, Yantai, China, 2010.
W. Cai and Z. Wei, “Pii GAN: generative adversarial networks for pluralistic image inpainting,” IEEE Access, vol. 8, pp. 48451–48463, 2019.
Kausar, A., Sharif, M., Park, J., & Shin, D. R. (2018, December). Pure-cnn: A framework for fruit images classification. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 404-408). IEEE.
Prof. Vaishali Sarangpure. (2018). Hybrid Hand-off Scheme for Performance Improvisation of Wireless Networks. International Journal of New Practices in Management and Engineering, 7(03), 08 - 14. https://doi.org/10.17762/ijnpme.v7i03.67
Martínez, L., Milić, M., Popova, E., Smit, S., & Goldberg, R. Machine Learning Approaches for Human Activity Recognition. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/146
Joshi, K., Kumar, V., Sundaresan, V., Ashish Kumar Karanam, S., Dhabliya, D., Daniel Shadrach, F., & Ramachandra, A. C. (2022). Intelligent fusion approach for MRI and CT imaging using CNN with wavelet transform approach. Paper presented at the IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022, doi:10.1109/ICKECS56523.2022.10060322 Retrieved from www.scopus.com
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