About This Project

Modern agriculture confronts a persistent challenge: pest infestations that compromise crop health and yield. Traditional pest control methods often prove inefficient and environmentally risky. To address this, an autonomous drone equipped with advanced sensing and spraying technologies offers a novel solution. This project examines the pest problem, introduces the drone's capabilities, highlights benefits for farmers, and emphasizes collaborative research that drives its development.

Ask the Scientists

What is the context of this research?

Improper pesticide application can lead to resistance development in weeds, non-target effects on ecosystems, environmental contamination through runoff and drift, health risks for humans and wildlife, residue accumulation, and volatility-driven contamination.

To tackle this challenge, I intend to create a self-operating drone capable of identifying ground-level weeds and applying targeted pesticide treatment. Through the integration of an Ardupilot and LiDAR technology, my drone can effectively pinpoint the location of weeds and apply precise amounts of pesticide exclusively to the targeted weed(s), ensuring the preservation of the surrounding crops.

What is the significance of this project?

The significance of a drone capable of autonomously identifying and spraying weeds lies in its potential to address a critical issue related to pesticide use. Currently, the indiscriminate application of pesticides across entire crop fields results in the contamination of crops with harmful chemicals. By utilizing a drone equipped with weed-detection technology, precise targeting of weeds becomes possible. This targeted approach minimizes the need for excessive pesticide use, reducing ecological risks, preserving crop quality, and ultimately ensuring safer and more sustainable food production.

What are the goals of the project?

By conducting controlled trials in natural agricultural settings, we seek to determine if the drone's autonomous weed detection and precision-spraying capabilities reduce chemical usage and a subsequent decline in chemical infiltration into crops. This will be done by measuring the amount of time it takes the drone to go and come back to its initial location, while also noting how long it takes for the drone to detect the weeds and spray the pesticide solution. We will conduct a comparative analysis with this data involving human intervention, wherein a person manually locates and sprays the weeds to assess the drone's overall robustness and performance.