An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced...

Area of Science:

• Agricultural Technology
• Plant Pathology
• Computer Vision

Background:

• Crop diseases pose a significant threat to agricultural sustainability and productivity.
• Precise plant health monitoring is crucial for effective disease management.
• Image processing offers a powerful tool for automated disease identification.

Purpose of the Study:

• To develop an innovative approach for precise detection and classification of fig leaf diseases.
• To combine Support Vector Machine (SVM) with advanced image processing techniques.
• To enhance agricultural sustainability through improved plant health monitoring.

Main Methods:

• Acquisition of digital color images of diseased fig leaves.
• Image preprocessing: denoising (mean function) and enhancement (Contrast-limited adaptive histogram equalization).
• Segmentation (Fuzzy C Means), feature extraction (Principal Component Analysis), and classification (PSO SVM, Backpropagation Neural Network, Random Forest).

Main Results:

• The Particle Swarm Optimization Support Vector Machine (PSO SVM) algorithm achieved exceptional performance in accurately classifying fig leaf diseases.
• The study demonstrated the effectiveness of the proposed integrated image processing and classification approach.
• High accuracy in detection and classification of fig leaf diseases was observed.

Conclusions:

• The integration of image processing and SVM-based classification provides a promising solution for crop disease management.
• This approach contributes significantly to sustainable agriculture and plant disease mitigation.
• The findings support the advancement of agricultural productivity and global food security through technology.