This has three main projects , which includes the Pest detection on palm tree, Mango harvesting and Monkey scaring. Introduction: Autonomous Drone System for Precision Agriculture and Wildlife Management The growing demands of modern agriculture and wildlife management necessitate the development of robots capable of performing tasks autonomously, with high accuracy and reliability. Traditional methods relying on manual labor are often time-consuming, labor-intensive, and sometimes ineffective.
This code addresses this challenge by outlining a drone-based system designed for three critical applications:
Precision Fruit Picking: This module focuses on the autonomous identification and harvesting of specific fruits, such as mangoes. The system will utilize manipulators to sever the fruit's stem with precision, minimizing damage to the plant and the fruit itself. Targeted Pest Eradication: This module equips drones with the ability to detect and eliminate animal pests that threaten crops. Using targeted spraying mechanisms, the system will deliver deterrents or pesticides with precision, minimizing environmental impact. Oil Palm Pest Management: This module tailors the system for effective pest control in oil palm plantations. The code will account for the unique characteristics of oil palms and their associated pests. The core functionality of this system lies in the seamless integration of manipulator capabilities and drone maneuverability. The code will define the control algorithms for precise drone movement and manipulator operation, ensuring efficient task execution in various agricultural environments. Monkey Scaring Mechanism: This module incorporates a system to deter monkeys and other wildlife from damaging crops. The implementation will likely involve a combination of visual and auditory stimuli, such as flashing lights and distress calls of predator animals.
This code serves as a framework for developing a comprehensive autonomous drone system that revolutionizes precision agriculture and wildlife management practices.