An Automated CONV-RFDNN Model Poised for the Differentiation and Identification of Diseases Impacting Mango Fruits through Transfer Learning
Keywords:
Mango Fruit, Deep Learning, Transfer Learning, Random ForestAbstract
Fruit diseases pose a significant threat to crop yield and overall fruit quality. This research aims to address the challenges associated with the manual inspection process for identifying mango diseases. By leveraging advanced technology and image processing techniques, the proposed automated inspection system seeks to improve efficiency, reduce dependence on manual labor, and ensure timely detection of diseases. The findings of this research can potentially contribute to enhanced fruit quality and increased mango yield, ultimately benefiting global food security and human health. This study adopts an image classification approach to identify various diseases in mangoes and distinguish them from healthy specimens. The pre-processing phase involves the use of a Gaussian filter for noise removal. Following pre-processing, color-based segmentation (RGB Thresholding) is employed as a crucial operation. Subsequently, features are extracted using the VGG-19 model. The proposed model undergoes experimental verification and validation, demonstrating optimal outcomes with a precision rate of 98%. This high precision rate showcases the effectiveness of the Random Forest classifier in accurately categorizing mango images into different disease categories. The experimental results support the potential practical application of the model in the agricultural industry for disease detection in mango crops.
Downloads
References
LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
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.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,” International journal of computer vision, vol. 115, no. 3, pp. 211–252, 2015.
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1285–1298, 2016.
Sema, W., Yayeh, Y., &Andualem, G. (2023). Automatic Detection and Classification of Mango Disease Using Convolutional Neural Network and Histogram Oriented Gradients.
Tran, V. L., Doan, T. N. C., Ferrero, F., Huy, T. L., & Le-Thanh, N. (2023). The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading.Sensors,23(2), 952.
Laxmi, V., &Roopalakshmi, R. (2022). Artificially Ripened Mango Fruit Prediction System Using Convolutional Neural Network. InIntelligent Systems and Sustainable Computing: Proceedings of ICISSC 2021(pp. 345-356). Singapore: Springer Nature Singapore.
Dandavate, R., &Patodkar, V. (2020, October). CNN and data augmentation based fruit classification model. In2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC)(pp. 784-787). IEEE.
Rojas-Aranda, J. L., Nunez-Varela, J. I., Cuevas-Tello, J. C., & Rangel-Ramirez, G. (2020). Fruit classification for retail stores using deep learning. InPattern Recognition: 12th Mexican Conference, MCPR 2020, Morelia, Mexico, June 24–27, 2020, Proceedings 12(pp. 3-13). Springer International Publishing.
Ummapure, S. B., &Hanchinal, S. M. (2020). Multi Features based Fruit Classification Using different Classifiers.Journal of University of Shanghai for Science and Technology,22(12), 1344-1356.
Thinh, N. T., Thong, N. D., & Cong, H. T. (2020). Sorting and Classification of Mangoes based on Artificial Intelligence.International Journal of Machine Learning and Computing,10(2).
Russo, Fabrizio. "A method for estimation and filtering of Gaussian noise in images." IEEE Transactions on Instrumentation and Measurement 52.4 (2003): 1148-1154.
Dong, Guo, and Ming Xie. "Color clustering and learning for image segmentation based on neural networks." IEEE transactions on neural networks 16.4 (2005): 925-936.
Vidyasri, S., and S. Saravanan. "An Automated CHNN Model for the Classification and Detection of Lung Diseases using Transfer Learning." 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023.
Shaheed, Kashif, et al. "Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier." Healthcare. Vol. 11. No. 6. MDPI, 2023.
https://www.kaggle.com/datasets/warcoder/mangofruitdds
Suhasini, A., and V. V. S. S. S. Balaram. "Detection and Classification of Disease from Mango fruit using Convolutional Recurrent Neural Network with Metaheruistic Optimizer." International Journal of Intelligent Systems and Applications in Engineering 12.9s (2024): 321-334.
U.S. House. 102nd Congress, 1st Session. (1991, Jan. 11). H. Con. Res. 1, Sense of the Congress on Approval of Military Action. [Online]. Available: LEXIS Library: GENFED File: BILLS
Musical toothbrush with mirror, by L.M.R. Brooks. (1992, May 19). Patent D 326 189 [Online]. Available: NEXIS Library: LEXPAT File: DES
D. B. Payne and J. R. Stern, “Wavelength-switched pas- sively coupled single-mode optical network,” in Proc. IOOC-ECOC, Boston, MA, USA, 1985,
pp. 585–590.
D. Ebehard and E. Voges, “Digital single sideband detection for interferometric sensors,” presented at the 2nd Int. Conf. Optical Fiber Sensors, Stuttgart, Germany, Jan. 2-5, 1984.
G. Brandli and M. Dick, “Alternating current fed power supply,” U.S. Patent 4 084 217, Nov. 4, 1978.
J. O. Williams, “Narrow-band analyzer,” Ph.D. dissertation, Dept. Elect. Eng., Harvard Univ., Cambridge, MA, USA, 1993.
N. Kawasaki, “Parametric study of thermal and chemical nonequilibrium nozzle flow,” M.S. thesis, Dept. Electron. Eng., Osaka Univ., Osaka, Japan, 1993.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
