Abstract
This study focuses on the vital task of detecting Banana Black Sigatoka in banana plants using a cutting-edge method that combines deep learning algorithms with Unmanned Aerial Vehicles (UAVs). The research includes building a detailed dataset that features images of both healthy and infected banana plants. A variety of deep learning algorithms, such as convolutional neural networks and residual networks, are thoroughly tested to select the most effective model for analyzing this dataset. The selected algorithm is then integrated into a UAV-based system for the real-time detection of Black Sigatoka within banana plantations. This proactive strategy allows for the quick detection and localization of affected plants, making it possible to intervene promptly and improve overall crop management. The proposed method marks a significant step forward in using technology for precision agriculture, aiming to enhance the resilience and productivity of banana farming.