A CONV-EGBDNN Model for the Classification and Detection of Mango Diseases on Diseased Mango Images utilizing Transfer Learning

Authors

  • Ramalingam Kalaivani Department of Computer and Information Science, Faculty of Science, Annamalai University, India
  • Arunachalam Saravanan Department of Computer and Information Science, Faculty of Science, Annamalai University, India
Volume: 14 | Issue: 3 | Pages: 14349-14354 | June 2024 | https://doi.org/10.48084/etasr.7327

Abstract

Mango fruits are highly valued for their taste, flavor, and nutritional value, making them a popular choice among consumers. However, mango fruits are susceptible to various diseases that can significantly affect their yield and quality. Therefore, accurate and timely detection of these diseases is crucial for effective disease management and minimizing losses in mango production. Computer-aided diagnosis techniques have emerged as a promising tool for disease detection and classification in mango fruits. This study adopts an image classification approach to identify various diseases in mangos and distinguish them from healthy specimens. The pre-processing phase involves a Wiener filter for noise removal, followed by Otsu's threshold-based segmentation as a crucial operation. Subsequently, features are extracted by implementing the ResNet50 model. The proposed model was experimentally verified and validated, demonstrating optimal results with an accuracy of 98.25%. This high accuracy rate highlights the effectiveness of the XG-Boost classifier in accurately categorizing mango images into different disease categories. The experimental results strongly support the potential practical application of the model in the agricultural industry for disease detection in mango crops.

Keywords:

deep learning, mango fruit, XG-Boost, transfer learning, ResNet50

Downloads

Download data is not yet available.

References

M. K. Islam, A. K. M. R. Islam, M. A. R. Sarkar, M. Z. H. Khan, and S. Yeasmin, "Changes in Color and Physiological Components of The Postharvest Mango (Mangifera indica L.) Influenced by Different Levels of GA3," Aceh International Journal of Science and Technology, vol. 2, no. 2, pp. 70–76, Aug. 2013.

D. Sivakumar, Y. Jiang, and E. M. Yahia, "Maintaining mango (Mangifera indica L.) fruit quality during the export chain," Food Research International, vol. 44, no. 5, pp. 1254–1263, Jun. 2011.

V. Kour, S. Arora, and J. Pal Singh, "A survey on vision based techniques for detection and classification of fruit diseases," International Journal of Engineering & Technology, vol. 7, no. 4.5, pp. 506-510, Sep. 2018.

M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, and M. Y. Javed, "Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection," Computers and Electronics in Agriculture, vol. 150, pp. 220–234, Jul. 2018.

S. Gulavnai and R. Patil, "Deep learning for image based mango leaf disease detection," International Journal of Recent Technology and Engineering, vol. 8, no. 3S3, pp. 54–56, 2019.

S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022.

S. Nuanmeesri, "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7678–7683, Oct. 2021.

D. R. F. R. Anandhi and S. Sathiamoorthy, "Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11959–11964, Oct. 2023.

E. H. Yossy, J. Pranata, T. Wijaya, H. Hermawan, and W. Budiharto, "Mango Fruit Sortation System using Neural Network and Computer Vision," Procedia Computer Science, vol. 116, pp. 596–603, Jan. 2017.

S. S. Veling, R. S. Kalekhar, L. V. Ajgaonkar, N. V. Mestry, and N. N. Gawade, "Mango Disease Detection by using Image Processing," International Journal for Research in Applied Science and Engineering Technology, vol. 7, no. 4, pp. 3717–3726, Apr. 2019.

S. Dhameliya, J. Kakadiya, and R. Savant, "Volume Estimation of Mango," International Journal of Computer Applications, vol. 143, no. 12, pp. 11–16, Jun. 2016.

S. B. Ullagaddi and S. V. Raju, "An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop," International Journal of Image, Graphics and Signal Processing, vol. 9, no. 9, pp. 28-39, Sep. 2017.

S. B. Ullagaddi and S. V. Raju, "Disease recognition in Mango crop using modified rotational kernel transform features," in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, Jan. 2017, pp. 1–8.

A. S. Nadarajan and A. Thamizharasi, "Detection of Bacterial Canker Disease in Mango using image processing," IOSR Journal of Computer Engineering (IOSR-JCE), vol. 1, pp. 901–08, 2017.

H. Zheng and H. Lu, "A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)," Computers and Electronics in Agriculture, vol. 83, pp. 47–51, Apr. 2012.

I. M. Hassoon, "Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review," Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 14, no. 4, 2022, Accessed: Apr. 2024.

N. Kumari, A. K. Bhatt, R. K. Dwivedi, and R. Belwal, "Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation," Environment Conservation Journal, vol. 21, no. 3, pp. 17–23, Dec. 2020.

S. Behera, A. Rath, and P. Sethy, "Fruit Recognition using Support Vector Machine based on Deep Features," Karbala International Journal of Modern Science, vol. 6, no. 2, Jun. 2020.

S. S, B. Ananthakrishnan, and A. K. Sivaraman, "Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, pp. 70–79, Jun. 2022.

C. Chauhan, "MangoFruitDDS." [Online]. Available: https://www.kaggle.com/datasets/warcoder/mangofruitdds.

V. K. Reddy, A. Suhasini, 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, vol. 12, no. 9s, pp. 321–334, 2024.

Downloads

How to Cite

[1]
Kalaivani, R. and Saravanan, A. 2024. A CONV-EGBDNN Model for the Classification and Detection of Mango Diseases on Diseased Mango Images utilizing Transfer Learning. Engineering, Technology & Applied Science Research. 14, 3 (Jun. 2024), 14349–14354. DOI:https://doi.org/10.48084/etasr.7327.

Metrics

Abstract Views: 339
PDF Downloads: 355

Metrics Information

Most read articles by the same author(s)