Revolutionizing Mango Leaf Disease Detection: Leveraging Segmentation and Hybrid Deep Learning for Enhanced Accuracy and Sustainability
Keywords:
Mango leaf, leaf disease detection, deep learning, image segmentation, cropsAbstract
The time and effort saved by farmers thanks to automatic plant disease diagnosis is substantial. In agriculture, identifying plant diseases is crucial for increasing both the quality and quantity of harvests. Due to their importance as a plant’s food source, spotting leaf illnesses as soon as possible is crucial. The implementation of automation in the detection and management of plant diseases has proven to be advantageous, as it minimizes the need for extensive monitoring efforts in vast agricultural settings. So far, the research was done on single class or maximum of 4 classes of same location. The present study employs an Hybrid Deep learning methodology to automate the detection of eight different leaf diseases in mango trees. A dataset comprising 4873 images collected from mendley and local locations of India. The Images of healthy and ailing mango leaves has been analyzed, revealing the presence of eight distinct leaf diseases, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Red rust, and Sooty Mould. The hybrid model presented in this study demonstrates a 93.01% accuracy rate in recognizing leaf diseases in mango plants, indicating its potential for practical implementation in real-time applications.
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P. K. Mishra and A. Singh, "REVEALED COMPARATIVE ADVANTAGE (RCA) AND ITS APPLICATION TO EVALUATE INDIA’S PERFORMANCE OF FRESH MANGOES, MANGOSTEEN & GUAVAS DURING THE PERIOD 1991-2020: AN ANALYSIS WITH RESPECT TO TRADE," Journal of Contemporary Issues in Business Government, vol. 29, no. 1, pp. 396-421, 2023.
M. Mohapatra, A. K. Parida, P. K. Mallick, M. Zymbler, and S. Kumar, "Botanical leaf disease detection and classification using convolutional neural network: a hybrid metaheuristic enabled approach," Computers, vol. 11, no. 5, p. 82, 2022.
Y. Wu, J.-H. Cheng, K. M. Keener, and D.-W. Sun, "Inhibitory effects of dielectric barrier discharge cold plasma on pathogenic enzymes and anthracnose for mango postharvest preservation," Postharvest Biology Technology, vol. 196, p. 112181, 2023.
G. N. Agrios, Plant pathology. Elsevier, 2005.
C. F. Nabillah, A. P. Cahyani, D. E. Pranoto, D. B. Kusumawati, F. I. Rochman, and P. Suryaminarsih, "Analysis of Interactions Between Components Catfish (Clarias batrachus), Man-go (Mangifera indica), Banana (Musa paradisiaca), and Jabon (Neolamarckia cadamba) and Their Relationship to Pest Dynamic in Agrosilvopastural System, Tamiajeng Village, Trawas," Nusantara Science Technology Proceedings, pp. 60-64, 2023.
F. Jiang et al., "Artificial intelligence in healthcare: past, present and future," Stroke vascular neurology, vol. 2, no. 4, 2017.
T. Rumpf, A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, and L. Plümer, "Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance," Computers electronics in agriculture, vol. 74, no. 1, pp. 91-99, 2010.
H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, "Fast and accurate detection and classification of plant diseases," International Journal of Computer Applications, vol. 17, no. 1, pp. 31-38, 2011.
P. Revathi and M. Hemalatha, "Identification of cotton diseases based on cross information gain deep forward neural network classifier with PSO feature selection," International Journal of Engineering Technology, vol. 5, no. 6, pp. 4637-4642, 2014.
U. Mokhtar, M. A. Ali, A. E. Hassanien, and H. Hefny, "Identifying two of tomatoes leaf viruses using support vector machine," in Information Systems Design and Intelligent Applications: Proceedings of Second International Conference INDIA 2015, Volume 1, 2015, pp. 771-782: Springer.
V. Sharma, A. K. Tripathi, and H. Mittal, "DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection," Ecological Informatics, vol. 75, p. 102025, 2023.
S. I. Ahmed et al., "MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves," vol. 47, p. 108941, 2023.
M. A. Khan, T. Akram, M. Sharif, M. Alhaisoni, T. Saba, and N. Nawaz, "A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases," EURASIP Journal on Image Video Processing, vol. 2021, no. 1, pp. 1-28, 2021.
S. Arivazhagan, R. N. Shebiah, S. Ananthi, and S. V. Varthini, "Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features," Agricultural Engineering International: CIGR Journal, vol. 15, no. 1, pp. 211-217, 2013.
K. Fulsoundar, T. Kadlag, S. Bhadale, P. Bharvirkar, and S. Godse, "Detection and classification of plant leaf diseases," International Journal of Engineering Research General Science, vol. 2, no. 6, pp. 868-874, 2014.
S. Phadikar and J. Sil, "Rice disease identification using pattern recognition techniques," in 2008 11th International Conference on Computer and Information Technology, 2008, pp. 420-423: IEEE.
R. Preethi, S. Priyanka, U. Priyanka, and A. Sheela, "Efficient knowledge based system for leaf disease detection and classification," International Journal in Advanced Research in Science, Engineering and Technology, vol. 4, pp. 1134-1143, 2015.
Z. Iqbal, M. A. Khan, M. Sharif, J. H. Shah, M. H. ur Rehman, and K. Javed, "An automated detection and classification of citrus plant diseases using image processing techniques: A review," Computers Electronics in Agriculture, vol. 153, pp. 12-32, 2018.
J. Shin, Y. K. Chang, B. Heung, T. Nguyen-Quang, G. W. Price, and A. Al-Mallahi, "Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection," Biosystems Engineering, vol. 194, pp. 49-60, 2020.
H. Lin, H. Sheng, G. Sun, Y. Li, M. Xiao, and X. Wang, "Identification of pumpkin powdery mildew based on image processing PCA and machine learning," Multimedia Tools Applications, vol. 80, pp. 21085-21099, 2021.
T. N. Pham, L. Van Tran, and S. V. T. Dao, "Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection," IEEE Access, vol. 8, pp. 189960-189973, 2020.
M. R. Mia, S. Roy, S. K. Das, and M. A. Rahman, "Mango leaf disease recognition using neural network and support vector machine," Iran Journal of Computer Science, vol. 3, pp. 185-193, 2020.
K. Srunitha and D. Bharathi, "Mango leaf unhealthy region detection and classification," in Computational Vision and Bio Inspired Computing, 2018, pp. 422-436: Springer.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in 2017 international conference on engineering and technology (ICET), 2017, pp. 1-6: Ieee.
R. Chauhan, K. K. Ghanshala, and R. Joshi, "Convolutional neural network (CNN) for image detection and recognition," in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 278-282: IEEE.
M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning, 2019, pp. 6105-6114: PMLR.
C. Trongtorkid and P. Pramokchon, "Expert system for diagnosis mango diseases using leaf symptoms analysis," in 2018 International Conference on Digital Arts, Media and Technology (ICDAMT), 2018, pp. 59-64: IEEE.
G. S. Tumang, "Pests and diseases identification in mango using MATLAB," in 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019, pp. 1-4: IEEE.
S. Gulavnai, R. J. I. J. o. R. T. Patil, and Engineering, "Deep Learning for Image Based Mango Leaf Disease Detection," International Journal of Recent Technology and Engineering, vol. 8, pp. 54-56, 2019.
M. Prabu and B. J. Chelliah, "Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm," Neural Computing Applications, vol. 34, no. 9, pp. 7311-7324, 2022.
Allauddin Mulla, R. ., Eknath Pawar, M. ., S. Banait, S. ., N. Ajani, S. ., Pravin Borawake, M. ., & Hundekari, S. . (2023). Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 278–285. https://doi.org/10.17762/ijritcc.v11i2s.6147
Jackson, B., Lewis, M., Herrera, J., Fernández, M., & González, A. Machine Learning Applications for Performance Evaluation in Engineering Management. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/126
Sindhwani, N., Anand, R., Vashisth, R., Chauhan, S., Talukdar, V., & Dhabliya, D. (2022). Thingspeak-based environmental monitoring system using IoT. Paper presented at the PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 675-680. doi:10.1109/PDGC56933.2022.10053167 Retrieved from www.scopus.com
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