A Swarm Intelligence-Based Model for Disease Detection in Mango Crops

• Published in: 2022 1st International Conference on Computational Science and Technology (ICCST) pp. 845 - 850
• Main Authors: Rao, K Venkateswara, Kiran, Palakeeti, Praveena, Murala, Sreenath, Kocharla, Anusha, Pureti, Kumar, Eedupalli Sai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.11.2022
• Subjects: Crops; Expert System; Image segmentation; Mango leaf diseases; Mathematical models; Microorganisms; Minimum Distance Classifier; Particle swarm optimization; Population-based Swarm Intelligence; Scientific computing; Soft Computing Techniques; Support vector machines; Swarm Intelligence
• DOI: 10.1109/ICCST55948.2022.10040428

Soft Computing is the combination of approaches that are used to develop solutions to real-world problems that are not easy to model mathematically. The k-means technique is used for picture fragmentation in the current system, while the support vector machine classifier (SVM) classifier or the random forest classifier is used to identify diseases. The existing systems had conducted experiments on only one type of disease mainly Anthracnose. In the proposed system, image segmentation is done using Particle Swarm Optimization (PSO) and disease identification is done using a minimum distance classifier. In this paper, different diseases and various images of mango leaves are considered for the study. Experiments showed that the suggested approach had an accuracy of 92.5% when testing for six different maladies: Bacterial Canker (BC), Odium Mangifera (OM), Mango Anthracnose (MA), Mango Malformation (MM), Powdery Mildew (PM), and Sooty Mould (SM).