Problem Statement
Traditional agriculture methods often lack precision in monitoring crop health and
resource utilization. Farmers require advanced tools for efficient and data-driven
decision-making. This project aims to address these challenges by creating an
AI-powered fleet of drones for precision agriculture.
Abstract
The AI-powered Agricultural Drone Fleet project focuses on revolutionizing traditional
farming practices. The fleet of drones will be equipped with advanced sensors and AI
algorithms to monitor crop health, detect diseases, optimize irrigation, and assess
overall field conditions. The system aims to provide farmers with real-time insights,
allowing them to make informed decisions for resource-efficient and sustainable
agriculture.
Outcome
● Autonomous Drone Fleet: Develop a fleet of drones capable of autonomous flight
and data collection.
● Crop Health Monitoring: Implement AI algorithms for analyzing drone-captured
data to assess crop health and detect anomalies.
● Resource Optimization: Optimize resource usage, including water and fertilizers,
based on the analyzed data.
● User-Friendly Interface: Provide farmers with an intuitive interface for accessing
real-time data and recommendations.
● Increased Crop Yield: Ultimately, enhance crop yield through precision agriculture
practices.
Reference
Agriculture drones offer clear advantages over other monitoring methods including satellite imaging, manned scouting, and manned aircraft. However, for large scale areas, such as large forestry and agriculture mapping problems, the single drone is hard to accomplish its mission of mapping in a relatively short time period of 30 to 45 minutes. In addition, in large forestry mapping, camera, communication, and payload settings may further reduce the maximum endurance of drones in the air. With a single drone, the total required mission time to cover all the area is prolonged, not only producing a high cost for a drone service provider but also having more uncertainty. While with multiple drones, or a fleet of drones, it is possible to identify a globally optimized solution to reduce the total required mission time. In this paper, we mainly discuss the strategy of drone fleet deployment for large scale area surveying. Three key parts are analyzed, including a fleet of drones, cooperative coverage path planning, communication and data processing. The associated state-of-the-art solutions are listed and reviewed. In addition, in this paper, the key operational constraints for large scale agriculture and forestry surveying are analyzed. It should be pointed out that, from a comprehensive point of view, a drone fleet deployment for large scale surveying could attract more attention from the commercial drone industry.
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https://ieeexplore.ieee.org/document/8917235