Tutorial for Plant Level Data using Cabbages
Setting up the Data
Download the sample imagery for this tutorial by contacting Solvi : support@solvi.ag
Assuming you have already created your first ‘Farm’ within Solvi, select ‘Upload Images’, then ‘Stitched Map’ and navigate to the sample imagery location.
Populate the ‘Field’ and ‘Crop’ values, then select ‘Upload 1 Image’. Allow the upload and processing to complete.
Open the project within the ‘Imagery’ tab then define the Field Boundary. (This will make further processing more efficient)
NOTE If you are working over a particularly large area, then the processing stages are likely to take a long time. This being the case, we can recommend you reduce the field perimeter to a smaller area at first to test your AI model. Once successful, you can reset the boundaries.
You are now ready to begin using the PlantAI tool!
Running the Plant Level Data PlantAI Tool
Navigate to the ‘Analysis’ tab, select ‘PlantAI’ and choose to create a new detection. Select ‘Counts with Plant Level Data’. The ‘Provide Examples’ setup will appear
Zoom into the orthomosaic image choosing an area of the orthomosaic image where there is a presence of both the desired plant alongside other elements such as weeds, soil, etc to better train the AI model.
Using the circle or polygon tool, draw a perimeter around 3 plant specimens (pumpkins). A perimeter box will then appear automatically afterward.
Note that it is best to use the polygon tool in those cases where the plant has an irregular shape.
At this point, you can either keep using the box tool, drawing a box around each plant shape OR you can use the select tool (arrow icon) to select the blue perimeter box (it’s colour will change to red) then select the magic wand tool , which will try and find more plant specimens automatically.
Ensure that all plant specimens visible within the bounding box are selected
The Review Test Results screen will be displayed where a manual inspection of the detection accuracy is required.
If there are any plants that have not been properly detected, zoom into this area, select ‘Provide more examples’ then select ‘add examples’.
To speed up this process, we can use results from the previous iteration. Once the bounding box has been created, use the select tool (arrow icon) to select the bounding box (its colour will change from blue to red), then click the “Get from Latest Results” button.
This will try to automatically find plants. Afterward, you can manually add additional plants using the box tool. When finished, select ‘continue’, then ‘Run Test Detection’ to test this new iteration of the training data.
NOTE The red outlines from the previous detection are only for reference, all new examples require that all plants within the bounding box to be selected again from new.
Continue this process improving the detection until the test area detects all plants correctly. Once you are satisfied with the results, then click on ‘Continue with the whole field’.
Now that the plants have been successfully detected, choose the plant specific metrics and indices that are of interest, then choose ‘Run Detection’
Note that at this stage, you can reduce the field boundaries to reduce the ‘cost’ of the process.
The final results will be displayed. If there remain undetected plants or false positives, select ‘provide more examples’ and repeat the process from before, otherwise select ‘Proceed to Analysis’
Analysis
The processing phase is complete and the plants can now be individually analysed for various metrics such as area, diameter, etc. Select the ‘Analysis’ tab, enable ‘Plant Counts’ and for this example choose ‘Diameter’. The results are shown graphically within the browser.
Congratulations! You have successfully reached the end of the plant level data workflow for PlantAI with Solvi. You can continue to explore the functionality of this tool by looking at the ‘Plant Health’ option, adjusting the Plant Count classification metrics or creating and analysing areas of interest using the ‘Zonal Statistics’ option.