The Proofminder AI models can precisely assess yields, identify GPS coordinates of plants and weeds, create prescription maps for spot spraying, monitor vegetation status or plant health to prevent losses and evaluate damages caused by wildlife, insects, or weather, among other factors. This collaborative effort ensures precision farming and sustainable agricultural practices, contributing significantly to the green revolution in agriculture.
The Proofminder platform extracts insights from drone images using AI to provide growers with valuable information about every square centimeter of field across the season. The company's technology can be used for field crops and vegetables, trees, orchards, or viticulture on fields of any size.
The company aims to increase crop yields, improve agricultural sustainability, and contribute to green change in farming practices by optimizing decision-making and reducing the use of chemicals. For data analysis, it is crucial to have high-resolution images of agricultural fields. One of the most cost-effective ways of gathering them is by performing automated low-altitude flights with camera-equipped drones.
The workflow for drone data collection from massive agricultural area scale
Flight preparations - to ensure efficient data collection:
- Obtaining the field contour for the flight mission or creating it based on high-quality images. Dividing the field into smaller segments for efficient coverage.
- Generating segments for the flights that are achievable with one battery. This allows to provide the routes to the contractors, and they don't have to deal with the nuisance of restarting a flight. In addition, this makes it possible to have multiple drone pilots working on the same data collection mission.
Drone mission planning and dispatch - to optimize the data collection process:
- Automated mission planning and checks for compliance with flight rules (airspace crossing, no powers, etc.).
- Providing on-call support to contracted drone pilots for any necessary adjustments to the flight plan. It is ok if a new adjusted flight plan is made available to the pilots within two minutes or even less.
Drone data gathering - to maximize productivity:
- Pilots execute the mission and upload the collected data to processing-
- Managing battery usage to enable continuous flights between segments.
Data processing - to streamline crop assessment:
- Generating orthomosaic for crop counting.
- Utilizing AI algorithms. In the example below – the AI model to count plants identifies distancing between plants in centimeters and missed plants. Every single crop or weed in the report has its own GPS coordinates for the immediate actions of growers.
Challenges in Obtaining High-Quality Imagery for AI Analysis
When it comes to obtaining high-quality imagery for analytical purposes, several challenges need to be addressed:
- Maintaining the same quality of data and the same resolution from every piece of land for correct data processing AI analysis.
- Requiring multiple pilots or drones simultaneously to meet deadlines and cover the large areas efficiently.
Overcoming these challenges becomes essential to ensure timely and efficient coverage of large areas while meeting project deadlines.
UgCS helps to maximize Large-Scale Data Gathering in Agriculture
By optimizing flight planning and field operations, UgCS allows to improve the data collection process. This, in turn, leads to more accurate data and better end-results. The key features in this process are:
- Dividing the mission into smaller segments for each operator or drone. It is convenient and quick to do in the 3D interface on a desktop.
- Simultaneous connection of up to 10 drones for route planning and execution. Utilizing more drones simultaneously leads to valuable time savings. (Available in UgCS ENTERPRISE license)
- Advanced terrain following. UgCS comes with one of the best drone terrain following algorithms, allowing to maintain a constant distance from the terrain or surface to capture high-quality images suitable for AI processing.
UgCS empowers agricultural professionals to gather reliable data on a large scale, improving how data acquisition is approached in the agricultural sector. By prioritizing pre-flight preparation, agricultural operators can streamline their processes, make informed decisions, and unlock new possibilities for improved productivity and sustainable practices.
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