An intelligent deep augmented model for detection of banana leaves diseases.
Journal:
Microscopy research and technique
PMID:
39177052
Abstract
One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healthy, Cordana, Sigatoka, and Pestalotiopsis. These types can be analyzed using four types of vision: RGB, night vision, infrared vision, and thermal vision. This paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. Each vision consisted of 1600 images with a size of (224 × 224). The training-testing approach was used to evaluate the performance of the hybrid model on Kaggle dataset, which was justified by various methods and metrics. The proposed model achieved a remarkable mean accuracy rate of 99.16% for RGB vision, 98.02% for night vision, 96.05% for infrared vision, and 96.10% for thermal vision for training and testing data. Microscopy employed in this research as a validation tool. The microscopic examination of leaves confirmed the presence and extent of the disease, providing ground truth data to validate and refine the proposed model. RESEARCH HIGHLIGHTS: The model can be helpful for internet of things -based drones to identify the large scale of banana leaf-disease detection using drones for images acquisition. Proposed an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. The model detected banana leaf disease with a 99.16% accuracy rate for RGB vision, 98.02% accuracy rate for night vision, 96.05% accuracy rate for infrared vision, and 96.10% accuracy rate for thermal vision The model will provide a facility for early disease detection which minimizes crop loss, enhances crop quality, timely decision making, cost saving, risk mitigation, technology adoption, and helps in increasing the yield.