4 steps to achieve high precision weed control with drones

GeoWeave was present at a hackathon where consultants and experts explored the end-to-end process of using drones for precision weed management. The event, organized by ILVO and VITO remote sensing, highlighted the steps required to convert drone-captured field data into actionable weed control maps, known as task maps, which can then be used to guide agricultural machinery.

Step 1: Drone Flight & Data Capture

During the hackathon, the team focused on planning and executing a drone mission over an agricultural plot. We were explained how the drone sampling technique can limit costs. These flights can be done by small, high-resolution drones, to capture detailed images of the fields. Variables such as flying altitude, weather conditions, and the crop growth stage were considered to optimize data quality

Step 2: Image Processing & AI Analysis

Once the drone data was collected, it underwent processing to create high-resolution orthomosaics—stitching together individual images into one comprehensive map. Using AI models, the data was analyzed to detect weed patterns, distinguishing between crop types and undesirable weeds. The AI-based models employed object detection and relative label models to score the weed presence in different areas of the field, providing a nuanced view of where intervention was necessary.

Step 3: Task Map Creation

After the analysis phase, the weed detection results were translated into task maps, which are used to guide the application of herbicides. These maps provide the machinery with precise information on which areas of the field require treatment and with what intensity. This step is essential to ensure the efficient use of resources, reducing both the environmental impact and costs.

Step 4: Machine Application

The final step involved integrating the task maps into agricultural spraying machines. The data from the task maps were converted into formats such as ISO-XML, ensuring compatibility with modern agricultural machinery. Once uploaded, these machines automatically adjust their spray rates according to the map’s instructions, applying herbicides only where needed and avoiding areas that don’t require treatment.

Lessons from the Hackathon

Throughout the event, consultants were exposed to the technical aspects of working with drone data, image processing tools like QGIS, and machine learning models. They also explored the challenges, such as handling varying image quality due to weather or flight conditions, and the importance of calibration and validation.

GeoWeave provides environmental and agricultural GIS solutions by combining geospatial data with digital expertise and business intelligence. We seamlessly integrate existing data components from our partners to create clear, cohesive solutions.
Get in touch to discuss more.

Step 1: Drone Flight & Data Capture

Step 2: Image Processing & AI Analysis

Step 3: Task Map Creation

Step 4: Machine Application

Lessons from the Hackathon

Related Articles