Drone Based Coconut Tree Monitoring System

This paper presents an innovative drone based system designed for the early identification and control of pests and diseases in coconut farms with the goal of minimizing crop loss and increasing production. The technology uses drones equipped with high-resolution cameras to take detailed pictures of coconut trees from various perspectives. These photos are then processed in real time using computer vision and machine learning algorithms that are especially designed to identify disease trends. Furthermore, the drones have acoustic sensors that use unique sounds made by pests as they burrow into trees to identify them, including the red palm weevil and rhinoceros beetle. Advanced signal processing techniques filter out background noise to isolate pest-specific sounds, enhancing detection accuracy. By integrating both visual and auditory data, this system provides a comprehensive assessment of tree health, improving the reliability of disease and pest detection. Because of its adaptability and scalability, the system can be used on plantations of different sizes, saving time and effort compared to traditional inspections. Real-time data analysis allows for prompt intervention, such as targeted pesticide application, which minimizes crop damage and reduces pesticide use. This approach supports sustainable agricultural practices while enhancing productivity. The potential of this drone-based system to continuously improve through machine learning further underscores its value as a forward looking tool in sustainable agriculture, providing a model for efficient resource management and disease control in coconut farming.