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Growers strive for homogeneous emergence, as this is key to maximum yield. The quality of sowing basically determines the success of homogeneous germination, which is a critical factor for the later life of the crop.
Unfortunately, for the time being, not everyone can afford to purchase the most modern, most precise and technologically innovative sowing machines of all time, thus ensuring the accuracy of sowing. The genetic background of seed hybrids can provide a solution to uneven plant spacing.
The purpose of precision agriculture is to provide optimal conditions for each plant, that is, to create harmony between soil conditions and existing technologies to utilize our area as efficiently as possible.
Figure 1 – Drone usage for plant stand count and plant distance measurement.
Manual plant distance measurement is a common method of calculating the number of plants and the spacing between them. This process assumes that the grower measures a distance manually on chosen field spots and makes assumptions. Modern agronomists already use digital scouting tools to estimate the plant number and distancing, but the biggest issue here is still the sampling approach and “guesstimations” but not solid data you can rely on to make confident decisions. In addition, none of the methods above provides information about issues or problem areas on the field, seed quality, plant performance or additional insights.
Differential seeding just makes this whole process more complex and impossible to handle with manual measures. In recent years, this technique has become increasingly popular in crop and seed production. Also known as precision seeding, it is becoming a more and more common practice. It involves adjusting the rate of seed dispersal based on the soil type and other factors, such as the rate of emergence and the size of the seed. This allows farmers to optimize the number of seeds they use, resulting in higher yields and improved crop quality.
Core practices and benefits of the differential seeding method
Having the exact data for comparing variability in planter unit performance or various seeds and improving the yield is extremely important.
Figure 2 – Proofminder AI-powered model for sowing quality and sowing zone analysis.
To address the challenges of seed and crop growers, we created our new AI model to measure the plant distance and plant gap analysis at scale, which generates 90%+ precise and actionable reports in just a few hours for fields of any size.
Proofminder approach
Our platform extracts data from highly precise aerial images to provide growers with actionable reports on the level of plants or leaves across the season. Why it is different:
In this article, we will describe our latest project for plant distancing and gap analysis with our new AI model for sugar beet, but the same approach could be applied for field crops, vegetables or trees.
Step 1. High-precise image collection
Here is the field of a large sugar beet producer in Hungary who is running various R&D projects across the season and has to measure and analyze tons of things. The challenge is to understand how different seed coatings perform on a given field. The method is to sow 6 rows of each coating and there are 50+ kinds of them. The goal is to see how they perform during the season, especially during and right after germination where the coating has a big role in preventing the seed from diseases and insects.
Using our AI model, we can evaluate the sowing quality and identify potential problems at the early stages.
Figure 3 – Image gathering with DJI Phantom 4 for sugar beet plant distancing analysis
The producer has multiple test fields to run the experiment in different conditions. Each of them is 3-8 hectares in size so counting and documenting the experiment is a huge manual and time-consuming task. It also must be super precise as the differences are sometimes small or minimal.
As the field sizes are not that big, we used smaller drones to capture the data. DJI Phantom 4 and DJI Mavic series drones with RGB cameras are well-capable drones for these kinds of data-capturing missions.
In these kinds of missions, we cannot compromise on image quality. Fortunately, even smaller DJI drones provide excellent image quality, and we can capture the needed 0.4-0.5 cm/px. Obviously, the best is to fly in sunny conditions and light wind.
Step 2. Data processing with AI-powered model for precise plant distance measuring
After uploading images to the Proofminder platform, it took a couple of hours to get the report with:
Figure 4 – Proofminder report: precise plant stand count
Figure 5 – Proofminder report: precise plant stand count
Figure 6 – Proofminder report: plant distancing in cm. and identified missed tassels
Step 3. Outcomes
The seed producer was able to assess the protective power and refine the coating formula, which is especially important as one of the main components in their current recipe will be banned in the EU soon, and they only have a few years to come up with an equivalent or better, greener solution.
Proofminder’s mission is to help create a more sustainable Agroindustry, and to enable growers to achieve production goals with confidence.
To try out our new algorithm for precise plant spacing measures, learn more about the platform capabilities or talk about your production challenges – book a Demo in our Calendar!
