A low-cost autonomous drone-based application, capable of detecting and spraying weeds in an agricultural setting

Backed by Sanjay Makkar
Raised of $1,089 Goal
Ended on 10/12/23
Campaign Ended
  • $250
  • 23%
  • Finished
    on 10/12/23

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.

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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.


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First step: Assemble the Drone using the materials. A hefty portion of the drone is allocated towards the Cube Orange+ w/ Here3+ & RFD900x Telemetry Set

Second Step: Combine the sprayer materials with the drone that was previously built out. Connecting the sprayer software is also completed.

Third Step: Attach the camera and connect it to the drone and sprayer. The software for the camera is also completed.

Endorsed by

With its advanced technology, the drone's precise weed detection and targeted pesticide spraying offer a game-changing solution. This not only boosts crop yields by minimizing weed competition but also reduces the need for widespread pesticide application, showcasing a significant stride towards sustainable and responsible farming practices.

Project Timeline

This project will begin once all the materials are finalized with the funding received. In anticipation of the science fair in March 2024, notebooks, research reports, and whitepapers are to be developed.

Aug 02, 2023

Project launched

Sep 01, 2023

Finalize financials

Sep 12, 2023

Project Launched

Oct 02, 2023

Order and receive materials

Oct 25, 2023

Build out the entire drone

Meet the Team

Akshin Makkar
Akshin Makkar
Student Researcher
Mr. T
Mr. T

Team Bio

Akshin Makkar


Mr. T


Additional Information

Project Backers

  • 1Backers
  • 23%Funded
  • $250Total Donations
  • $250.00Average Donation
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