Project Proposal: Smart Camera System for Agricultural Spraying Drones

Introduction: The use of drones in agriculture has increased in recent years, with drones being used for crop mapping, monitoring crop health, and spraying pesticides. However, current drone spraying methods can lead to inefficient use of pesticides and herbicides, which can cause harm to the environment and result in crop loss. To address this issue, we propose a smart camera system for agricultural spraying drones that can regulate the spraying in real time based on crop health data from satellites and real-time computer vision algorithms. The system will also detect non-crop regions of the field in real-time to ensure efficient and effective spraying.

Scope: The project will be divided into three main sections:

  1. Hardware Development: This will involve designing and integrating the necessary hardware components to build the smart camera system, including an edge computer, IMU, GPS, and a monocular camera. The hardware will be designed to be cost-efficient, lightweight, and easily integrable with existing agricultural spraying drone systems.
  2. Software Development: This section will involve designing and developing software for regulating the drone's sprayer based on the satellite-based prescription map. The software will be able to receive and interpret satellite data and make real-time decisions to adjust the drone's spraying pattern. The software will also integrate with the hardware components to ensure accurate and reliable operation.
  3. Perception Model Development: This section will involve developing a perception model to detect and localize non-crop regions in the farm, such as tractor tracks, sand spots, water holes, etc. This model will use real-time computer vision algorithms to analyze the monocular camera data and make accurate determinations of non-crop regions. The perception model will be integrated into the smart camera system to ensure effective and efficient spraying.

Methodology: The project will be conducted in the following stages:

  1. Requirements Gathering: This will involve gathering detailed requirements from stakeholders, including farmers, agricultural experts, and drone operators. The requirements will be used to guide the design and development of the smart camera system.
  2. Hardware Development: This stage will involve designing and integrating the hardware components of the smart camera system. The hardware will be tested to ensure it meets the requirements.
  3. Software Development: This stage will involve designing and developing the software for regulating the drone's sprayer based on the satellite-based prescription map. The software will be tested to ensure it meets the requirements.
  4. Perception Model Development: This stage will involve developing the perception model to detect and localize non-crop regions. The model will be tested to ensure it meets the requirements.
  5. Integration and Testing: This stage will involve integrating all components of the smart camera system and testing the system as a whole. The system will be tested in a real-world environment to ensure it meets the requirements.

Deliverables:

  1. Hardware: A fully functional smart camera system, including an edge computer, IMU, GPS, and a monocular camera.
  2. Software: A software program for regulating the drone's sprayer based on the satellite-based prescription map.
  3. Perception Model: A perception model for detecting and localizing non-crop regions in the farm.
  4. Documentation: Detailed documentation on the design, development, and testing of the smart camera system.

Conclusion: The proposed smart camera system for agricultural spraying drones has the potential to significantly improve the efficiency and accuracy of pesticide and herbicide use in agriculture. With its ability to regulate spraying in real-time based on crop health data from satellites and real-time computer vision algorithms, this system can help reduce environmental harm and crop loss while increasing crop yields. We believe that this project will contribute to the development of more sustainable and efficient farming practices.