Hybrid Particle Swarm and Chicken Swarm Optimization for Plant Disease Diagnosis using Leaf Images - Rana Muayad Hasan (SISTEMASI, 2026)
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.