Effectiveness of Deep Feature Extraction Algorithm in Determining the Maturity of Fruits: A Review
Article Sidebar
Main Article Content
Abstract
Intelligent farming technology helps farmers overcome tough obstacles in the farming process, such as increased sup-plier costs, a lack of labour, customer satisfaction, and more. Artificial Intelligence (AI) is a remarkable technology in smart farming because it deeply understands the issue and can help farmers make decisions. This article's main objective is to identify and examine the concepts and techniques of Convolutional Neural Networks (CNN) technology that could aid in classifying the ripeness stages of fruit in intelligent farming. This paper systematically reviews 18 previous works for classifying the ripeness stages of fruit. This review outlines the most commonly used algorithms, activation functions, optimisation functions, and platforms for algorithm implementation. In addition, found that not all algorithms are suitable for even near-equivalent processes. Therefore, this study suggests the intensity of the CNN algorithms concerning various metrics to find the suitability for the operations/applications. Finally, this paper offers some future research directions in the ripeness classification of fruits.
Article Details
References
Nelson, F., Pickett, T., Smith, W., & Ott, L. (2002). The GreenStar precision farming system. 1996 IEEE Position, Location and Navigation Symposium, PLANS '96 – Proceedings, 6–9. https://doi.org/10.1109/plans.1996.509048
Bloom, K. V., Smith, L. C., Elliott, C. A., & Boerhave, S. J. (2002). Precision Farming From Rockwell. 1996 IEEE Position, Location and Navigation Symposium, PLANS '96 - Proceedings. https://doi.org/10.1109/PLANS.1996.509047
Aherwadi, N., & Mittal, U. (2022). FRUIT QUALITY IDENTIFICATION USING IMAGE PROCESSING, MACHINE LEARNING, AND DEEP LEARNING: A REVIEW. Advances and Applications in Mathematical Sciences, 21(5), 2645–2660.
Naik, S., & Patel, B. (2017). Machine Vision based Fruit Classification and Grading - A Review. International Journal of Computer Applications, 170(9), 22–34. https://doi.org/10.5120/ijca2017914937
Hameed, K., Chai, D., & Rassau, A. (2018). A comprehensive review of fruit and vegetable classification techniques. Image and Vision Computing, 80, 24–44. https://doi.org/10.1016/j.imavis.2018.09.016
Gaikwad, R. S. ., & Gandage, S. . C. (2023). MCNN: Visual Sentiment Analysis using Various Deep Learning Framework with Deep CNN. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 265 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2625
Sekar, R. L., N.Ambika, V.Divya, & T.Kowsalya. (2018). Fruit Classification System Using Computer Vision: A Review. International Journal of Trend in Research and Development, 5(1), 2394–9333. www.ijtrd.com
K.Muthukannan, J.Petchiammal, S.Muthuveni, & A.Rajeshwari. (2018). An Image based analysis on fruit maturity-Review. International Journal of Scientific Development and Research, 3(4), 53–57. www.ijsdr.org
Kiran, M. S., & Niranjana, G. (2019). A Review on Fruit Maturity Detection Techniques. International Journal of Innovative Technology and Exploring Engineering, 8(6S). https://doi.org/10.11648/j.ajai.20170101.12
Wankhade, M., & Hore, U. W. (2021). A Survey on Fruit Ripeness Classification Based On Image Processing with Machine Learning. International Journal of Advanced Research in Science, Communication and Technology, 5(1), 73–78. https://doi.org/10.48175/ijarsct-1097
Charan.G, Ganesh.P, Dheeraj.MS, & N, S. . (2022). Survey on Real Time Fruit Detection and Classification using Image Processing and Convolution Neural Network. INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY, 8(11), 617–622.
Enrique Cueva Caro, J., & Isaac Necochea-chamorro, J. (2023). MACHINE LEARNING AND DEEP LEARNING FOR FRUIT IDENTIFICATION: SYSTEMATIC REVIEW. Journal of Theoretical and Applied Information Technology, 15(1). www.jatit.org
Lecun, Y., Bottou, E., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2323. https://doi.org/10.1109/5.726791
Dr. M. Varadharaj. (2019). Density Based Traffic Control System with Smart Sensing Of Emergency Vehicles. International Journal of New Practices in Management and Engineering, 8(02), 01 - 07. https://doi.org/10.17762/ijnpme.v8i02.75
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. International Conference on Neu- Ral Information Processing Systems, 1097–1105. https://doi.org/10.1145/3065386
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. http://arxiv.org/abs/1409.1556
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1–9. https://doi.org/10.1109/CVPR.2015.7298594
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.1109/CVPR.2016.90
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning Transferable Architectures for Scalable Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8697–8710. https://doi.org/10.1109/CVPR.2018.00907
Team, K. (n.d.-a). Keras Documentation: Keras applications. Keras. https://keras.io/api/applications/
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering - A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Aherwadi, N., Mittal, U., Singla, J., Jhanjhi, N. Z., Yassine, A., & Hossain, M. S. (2022). Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics (Switzerland), 11(24). https://doi.org/10.3390/electronics11244100
Benmouna, B., García-Mateos, G., Sabzi, S., Fernandez-Beltran, R., Parras-Burgos, D., & Molina-Martínez, J. M. (2022). Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy. Food and Bioprocess Technology, 15(10), 2226–2236. https://doi.org/10.1007/s11947-022-02880-7
Gururaj, N., Vinod, V., & Vijayakumar, K. (2022). Deep grading of mangoes using Convolutional Neural Network and Computer Vision. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-11616-2
Saranya, N., Srinivasan, K., & Kumar, S. K. P. (2022). Banana ripeness stage identification: a deep learning approach. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4033–4039. https://doi.org/10.1007/s12652-021-03267-w
Jackson, B., Lewis, M., González, M., Gonzalez, L., & González, M. Improving Natural Language Understanding with Transformer Models. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/152
Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188. https://doi.org/10.1016/j.compag.2021.106359
Faisal, M., Albogamy, F., Elgibreen, H., Algabri, M., & Alqershi, F. A. (2020). Deep Learning and Computer Vision for Estimating Date Fruits Type, Maturity Level, and Weight. IEEE Access, 8, 206770–206782. https://doi.org/10.1109/ACCESS.2020.3037948
Faisal, M., Alsulaiman, M., Arafah, M., & Mekhtiche, M. A. (2020). IHDS: Intelligent harvesting decision system for date fruit based on maturity stage using deep learning and computer vision. IEEE Access, 8, 167985–167997. https://doi.org/10.1109/ACCESS.2020.3023894
Altaheri, H., Alsulaiman, M., & Muhammad, G. (2019). Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning. IEEE Access, 7, 117115–117133. https://doi.org/10.1109/ACCESS.2019.2936536
Nasiri, A., Taheri-Garavand, A., & Zhang, Y. D. (2019). Image-based deep learning automated sorting of date fruit. Postharvest Biology and Technology, 153, 133–141. https://doi.org/10.1016/j.postharvbio.2019.04.003
Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244–250. https://doi.org/10.1016/j.inpa.2020.05.003
Garillos-Manliguez, C. A., & Chiang, J. Y. (2021). Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation. Sensors (Switzerland), 21(4), 1–18. https://doi.org/10.3390/s21041288
Dr. S.A. Sivakumar. (2019). Hybrid Design and RF Planning for 4G networks using Cell Prioritization Scheme. International Journal of New Practices in Management and Engineering, 8(02), 08 - 15. https://doi.org/10.17762/ijnpme.v8i02.76
Appe, S. R. N., Arulselvi, G., & Balaji, G. N. (2023). Tomato Ripeness Detection and Classification using VGG based CNN Models. Journal of Intelligent Systems and Applications in Engineering, 11(1), 296–302. www.ijisae.org
Su, F., Zhao, Y., Wang, G., Liu, P., Yan, Y., & Zu, L. (2022). Tomato Maturity Classification Based on SE-YOLOv3-MobileNetV1 Network under Nature Greenhouse Environment. Agronomy, 12(7). https://doi.org/10.3390/agronomy12071638
Cho, W. H., Kim, S. K., Na, M. H., & Na, I. S. (2021). Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset. Computers, Materials and Continua, 69(3), 4003–4024. https://doi.org/10.32604/cmc.2021.018758
Gao, Z., Shao, Y., Xuan, G., Wang, Y., Liu, Y., & Han, X. (2020). Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 4, 31–38. https://doi.org/10.1016/j.aiia.2020.04.003
Miraei Ashtiani, S. H., Javanmardi, S., Jahanbanifard, M., Martynenko, A., & Verbeek, F. J. (2021). Detection of mulberry ripeness stages using deep learning models. IEEE Access, 9, 100380–100394. https://doi.org/10.1109/ACCESS.2021.3096550
Zhao, H., Xu, D., Lawal, O., & Zhang, S. (2021). Muskmelon Maturity Stage Classification Model Based on CNN. Journal of Robotics, 2021. https://doi.org/10.1155/2021/8828340
Mahmood, A., Singh, S. K., & Tiwari, A. K. (2022). Pre-trained deep learning-based classification of jujube fruits according to their maturity level. Neural Computing and Applications, 34(16), 13925–13935. https://doi.org/10.1007/s00521-022-07213-5
Thompson, A., Walker, A., Rodriguez, C., Silva, D., & Castro, J. Machine Learning Approaches for Sentiment Analysis in Social Media. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/153
Miragaia, R., Chávez, F., Díaz, J., Vivas, A., Prieto, M. H., & Moñino, M. J. (2021). Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks. Agronomy, 11(11). https://doi.org/10.3390/agronomy
Team, K. (n.d.-b). Keras documentation: Optimizers. Keras. https://keras.io/api/optimizers/