Hybrid Particle Swarm and Chicken Swarm Optimization for Plant Disease Diagnosis using Leaf Images

Rana Muayad Hasan

Abstract


Background: Plants play a vital role in sustaining life on Earth, particularly in maintaining ecological balance. They also contribute significantly to the economies of many countries and provide other valuable benefits. These plants are susceptible to many different diseases. Traditionally, trained specialists diagnose plant diseases based on their experience. However, this approach leads to problems, as diagnoses can vary from person to person. Objective: This work aims to present a novel approach to reduce errors and eliminate guesswork using a hybrid method. Methodology: We have developed a novel hybrid method combining Particle Swarm Optimization (PSO) and Chicken Swarm Optimization (CSO) algorithms to diagnose plant diseases in a group of ten species based on images of their leaves. Therefore, images of leaves from ten different plant species were collected and processed to improve contrast and remove noise. Using a statistical method incorporating hybrid classification, features were extracted. The method was applied to leaf images of ten different plant species, such as guava, jamun, mango, grape, apple, tomato, argon, and cherry, using a MATLAB simulation program. Results: The results for the hybrid method (PSO-CSO) achieved a diagnostic accuracy of 98.9%. Conclusions: The results indicate that the proposed model is an effective tool for the automated diagnosis of plant diseases. Future efforts may include expanding the database to include new crops and integrating the model into mobile applications for immediate field use.

Keywords


chicken swarm optimization (CSO); image processing; MATLAB; particle swarm optimization (PSO); plant disease diagnosis

Full Text:

PDF

References


A. Thacharodi et al., "Microplastics in the Environment: A Critical Overview on Its Fate, Toxicity, Implications, Management, and Bioremediation Strategies," Journal of Environmental Management, Vol. 349, p. 119433, 2023.

B. M. Quach, V. C. Dinh, N. Pham, D. Huynh, and B. T. Nguyen, "Leaf Recognition using Convolutional Neural Networks based Features," Multimedia Tools and Applications, Vol. 82, No. 1, pp. 777–801, 2023.

A. Pal and V. Kumar, "AgriDet: Plant leaf Disease Severity Classification using Agriculture Detection Framework," Engineering Applications of Artificial Intelligence, 2023.

R. B. Vercellino, F. Hernández, C. Pandolfo, S. Ureta, and A. Presotto, "Agricultural Weeds: The Contribution of Domesticated Species to the Origin and Evolution of Feral Weeds," Pest Management Science, Vol. 79, No. 3, pp. 922–934, 2023.

R. M. Aziz, N. P. Desai, and M. F. Baluch, "Computer Vision Model with Novel Cuckoo Search based Deep Learning Approach for Classification of Fish Image," Multimedia Tools and Applications, Vol. 82, No. 3, pp. 3677–3696, 2023.

Y. Bai, B. Zhang, N. Xu, J. Zhou, J. Shi, and Z. Diao, "Vision-based Navigation and Guidance for Agricultural Autonomous Vehicles and Robots: A Review," Computers and Electronics in Agriculture, Vol. 205, p. 107584, 2023.

B. Zhang, J. Zhu, and H. Su, "Toward the Third Generation Artificial Intelligence," Science China Information Sciences, Vol. 66, No. 2, pp. 1–19, 2023.

R. M. Martins and C. G. Von Wangenheim, "Findings on Teaching Machine Learning in High School: A Ten-Year Systematic Literature Review," Informatics in Education, Vol. 22, No. 3, pp. 421–440, 2023.

Y. Hao, "Convolutional Neural Networks for Image Classification," in Proc. 2nd Int. Conf. Artificial Intelligence and Computer Engineering, pp. 342–345, 2021.

Y. Methkal et al., "Leaf Disease Identification and Classification using Optimized Deep Learning," Measurement: Sensors, Vol. 25, p. 100643, 2023.

A. Bonkra et al., "Apple Leaf Disease Detection using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis," International Journal of Environmental Research and Public Health, Vol. 20, No. 4, p. 119433, 2023.

S. S. Patil, S. H. Patil, A. M. Pawar, M. S. Bewoor, and N. S. Patil, "Comprehensive Review about Automatic Classification of Medicinal Plants," AIP Conference Proceedings, Vol. 2779, p. 11, 2023.

S. U. Rahman, F. Alam, N. Ahmad, and S. Arshad, "Image Processing based System for Detection and Classification of Tomato Leaf Diseases," Multimedia Tools and Applications, Vol. 82, No. 6, pp. 9431–9445, 2023.

M. P. Sikka, A. Sarkar, and S. Garg, "Artificial Intelligence in Textile Industry Operational Modernization," Research Journal of Textile and Apparel, Vol. 28, No. 1, pp. 67–83, 2024.

A. Habbal, M. K. Ali, and M. A. Abuzaraida, "AI TRiSM: Frameworks, Applications, Challenges and Future Directions," Expert Systems with Applications, Vol. 240, p. 122442, 2023.

Y. Y. Ghadi et al., "Machine Learning Solution for the Security of Wireless Sensor Networks," IEEE Access, Vol. 12, pp. 12699–12719, 2024.

L. N. and K. K. Saju, "Classification of Macronutrient Deficiencies in Maize Plant using Machine Learning," International Journal of Electrical and Computer Engineering, Vol. 8, No. 6, pp. 4197–4203, 2018.

V. Nguyen Thanh Le, B. Apopei, and K. Alameh, "Effective Plant Discrimination based on LBP and SVM Methods," Information Processing in Agriculture, Vol. 6, No. 1, pp. 116–131, 2019.

A. Bakhshipour and A. Jafari, "Evaluation of SVM and ANN in Weed Detection," Computers and Electronics in Agriculture, Vol. 145, pp. 153–160, 2018.

G. R. Dhingra, V. Kumar, and H. D. Joshi, "Neutrosophic Approach for Leaf Disease Classification," Measurement, Vol. 135, pp. 782–794, 2019.

M. Keivani, J. Mazloum, E. Sedaghatfar, and M. B. Tavakoli, "Automated Analysis of Leaf Shape, Texture, and Color Features," Traitement du Signal, Vol. 37, No. 1, pp. 17–28, 2020.

S. Pervaiz et al., "Comparative Research on PSO Initialization Techniques," Intelligent Automation & Soft Computing, Vol. 32, No. 3, pp. 1427–1444, 2022.

Z. Xu, J. Xia, and F. Zhong, "Adaptive Chicken Swarm Optimization Algorithm," Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 46, No. 1, 2024.

G. R. Shwetha and M. Svn, "Antlion Optimizer with PSO for Wireless Sensor Networks," Journal of Advances in Information Technology, Vol. 15, No. 1, pp. 17–26, 2024.

X. Meng, T. Lu, F. Min, and T. Lu, "Weighted Vector Median Filter for Impulse Noise Reduction," IET Image Processing, Vol. 15, No. 1, pp. 228–238, 2021.




DOI: https://doi.org/10.32520/stmsi.v15i8.6400

Article Metrics

Abstract view : 1 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.