Variable-rate spot spraying with AI-generated map and traditional sprayer

Cost saving - variable-rate spot spraying with a traditional sprayer? Yes, it is possible with the help of Proofminder!

Precision spraying is not only possible with drones and high-tech self-propelled sprayers. It is achievable for everyone with minimal preparation and a small workaround. If you have GPS and a traditional sprayer which is capable of section control, we have good news for you: you have the equipment to do spot praying and save up to 70% on chemicals.

Proofminder offers a solution for growers with traditional sprayers that will help them engage the full potential of their equipment, save significantly on weedkillers, and at the same time, reduce the impact of chemicals on the environment. From agronomical and economical perspectives, every drop of chemical that doesn’t land on the targeted weed is a waste. As such, it would be counterproductive and wasteful to use the same rate of chemicals on the entire field. The rate should be proportionate with the density of the targeted weeds. If we can reduce chemical usage by 50% on every hectare, we can spray twice as large of an area with the same amount and since fewer refills are needed, we can also save precious time. This also means cost saving in economically challenging times.

Figure 1 – Creeping thistle (Cirsium arvense) identified by AI in corn

If you own a commercially available drone, or you have access to a drone service provider in your area, all you will need to do is capture your field (we will provide the necessary specifications for the flight) before spraying and upload the pictures to Proofminder’s platform. Proofminder has a unique cloud-based platform which uses AI technology to identify different type of weeds and extract their coordinates. Using this tool Proofminder can generate a weed density map with the exact GPS coordinates of the different weeds. You may also filter for several types of weeds to decide which chemicals should be applied on different parts of your field.

Figure 2 – Creeping thistle spots identified by Proofminder AI model

As a next step, Proofminder exports the weeds’ coordinates to a format which is compatible with your traditional sprayer. You are now ready to utilize our spraying map with your machine. Enjoy your savings of time and money.

Figure 3 – John Deere sprayer. Source: www.deere.com

We performed a field trial in Hungary, focused on spot spraying. A drone service provider captured images of a larger farm and uploaded the images to Proofminder’s platform. The automated evaluation and result generation took place during the night after the image capturing mission, and the customer received the results the next morning.

The team responsible for spraying could now load the spraying map into their tractor which is operating a Horsch sprayer and is equipped with the John Deere controller.

Thanks to the spraying map provided by Proofminder, only the weed-infested portion of the field (67% percent) was sprayed and with that, 480 EUR/hectare was saved on chemicals and man hours.

This solution can be applied to several controllers of different manufacturers, and Proofminder is constantly working on extending the list of compatible controllers.

Pictured below, are two neighbouring fields with different weed densities where the mentioned field trial took place. The amount of chemicals applied is directly proportionate to weed density. The field on the left was only slightly infected with creeping thistle and as such, only a smaller portion of it had to be sprayed. However, the field on the right was heavily covered with thistle, but the weed density was varied, therefore a custom rate of chemicals was applied to save on chemicals, which could not have been achieved if the entire area would have been sprayed with an even amount.

Figure 4 – Prescription map generated by Proofminder AI model for variable spot spraying with traditional sprayer

Contact us today to implement the precision spot spraying and save your budget.

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Johnson’s grass and other weeds in corn: how to save up to 70% on chemicals

Johnson grass and other weeds in corn: how to save up to 70% on chemicals

Weed control plays a vital role in plant protection. Weeds represent a threat to cultivated plants, as they might shade them, drain nutrients and water from them and even hinder their ability to germinate. Furthermore, weeds can cause health issues and may also impede the harvesting process. “About 100s of euros of yield loss per hectare caused by johnson grass” – a production challenge that the grower raised and invited Proofminder to help.

Proofminder’s trained AI model can identify numerous weed species, such as johnson grass (Sorghum halepense), creeping thistle (Cirsium arvense), and jimsonweed (Datura stramonium) for example. In this case study, we will be focusing on the johnson grass recognition AI model in hybrid corn.

Corn is extremely sensitive to early weeding, as its ability to suppress weeds is still weak at that stage. Controlling johnson grass – one of the biggest threats to corn – is very important, as, if left uncontrolled, it can result in significant yield losses due to its ability to drain water and nutrients from the cultivated plants. Furthermore, johnson grass can be a host for the Maize chlorotic dwarf virus and Maize dwarf mosaic virus, and its allelopathic effect is also significant.

This case study focused on a 26.8-hectare hybrid corn field plagued by a johnson grass infestation. During the application of the traditional field spraying technology, the entire field is sprayed with a specified dose, averaging 200-300 litres per hectare. However, this approach is less aligned with the latest precision farming trends. Spot spraying can be a much more environmentally friendly and cost-effective solution, since many weeds, such as creeping thistle, occur in localized patches. Targeted spraying of these weed-infested patches is more easily applied and efficient as using pesticides should be sufficient.

A standard surveying drone (either the grower’s drone or Proofminder drone operator partner) conducts a comprehensive survey of the field, capturing high-resolution images.

Figure 1 – Johnson grass in corn, image from DJI drone

Figure 2 – Johnson grass in corn, field overview

With the help of our trained AI model, the captured images are analysed and the weeds are distinguished, with high precision, from cultivated plants. This analysis also shows the extent of the infestation on the field.

Figure 3 – Proofminder AI Model for johnson grass recognition

Figure 4 – Proofminder AI Model for johnson grass recognition, field overview

Based on the analysed data, a usage map is created, and presented in a standard geoinformatics format, such as a shapefile, which can then easily be downloaded to a machine sprayer or spraying drone.

Figure 5 – Proofminder weed map for hyper-precise spot spraying

Figure 6 – Spot spraying with DJI Agras T30 using Proofminder weed map

Out of the total area of 26.8 hectares, only 7.3 hectares 74 precise spots) had to be sprayed with graminicides. Spot spraying was made possible with a drone in this case. As weather conditions during the day were windy, which is less than ideal, spraying took place at night. The spraying mission was a success as no spray drift occurred, and 112 EUR/ha worth of chemicals could be saved.

Reach out to us to control weeds, save 40-70% on fertilisers, and stay ahead of the curve.

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Detasseling: from manual labor, days on end – to drones and AI

Detasseling: from manual labor, days on end - to drones and AI

Scientists and growers started experimenting with hybridizing seed corn in the early 20th century, and even back then, detasseling played a most significant role in the production process. In a mere century, we went from extensive manual labor, to introducing machined detasseling, to eventually having access to the latest technologies of drones and artificial intelligence.

Why is detasseling crucial in hybrid seed corn production?

Precise and timely detasseling – the removal of the tassels from all corn plants of one variety – results in a more physically uniform batch, increased yield, and higher genetic purity. Achieving a marketable purity rate of 99.7% leaves minimal room for error. The detasseling process is highly labor-intensive and very costly. This is where Proofminder steps in.

Figure 1 – Hybrid corn seed field after machine detasseling

Figure 2 – Missed tassel

Challenges addressed by Proofminder

Proofminder accurately identifies missed tassels and provides their exact GPS coordinates. We provide a kml file (Google Earth file) that can easily be viewed on any device. These files can provide helpful insights for agronomists, seed production managers, as well as manual detasseling crews to prioritize areas with missed tassels.
Proofminder helps reduce labor costs and time requirements.
Our solutions save cost and increase the yield.
We offer an environmentally friendly and sustainable approach to detasseling projects.
Our solutions can be seamlessly scaled to thousands of hectares of land.

Figure 3 – Missed tassels identified by Proofminder AI Model and marked on the field

Figure 4 – Missed tassels report built with Proofminder

Our process

A customized drone flight is planned specifically for your field. The drone’s camera is angled so that a much larger area is visible on the captured images.
Commercially available, off-the-shelf drones are adequate for tassel detection missions.
Proofminder can also arrange the drone flight part for you through our global network of drone service providers.
A single drone can capture and process over 100 hectares in a day.
Captured images are uploaded to the Proofminder platform.
Our trained AI model identifies male and female rows, enabling the exclusion of male plants during the missed tassel identification phase.
We provide comprehensive reports, including exact GPS locations and visualizations of missed female tassels. The GPS coordinates in the provided shapefiles can be loaded onto handheld GPS devices.
We deliver results in less than 24 hours. Since time is of the essence when it comes to detasseling, we capture images during the day and provide actionable reports before 6 AM next morning. This way, you can plan the day of your detasseling team in the most efficient way.

Figure 5 – Proofminder report: field overview with exact GPS coordinates of each missed tassel

Figure 6 – Exportable reports for the detasseling team

Your involvement

Image Capture: If you opt to take the images yourself, we will require high-definition images taken at a specific height and angle. We will provide the necessary specifications and handle the rest once we receive the images.
Proofminder’s Image Gathering Service: Alternatively, if you prefer, we can manage the entire image gathering process. All we need is a contour of the field in question and information regarding the sowing direction.

Figure 7 – Image gathering, flying over the corn field

Figure 8 – Image gathering using DJI Drone

About Proofminder

Proofminder enables Agroindustry players to transition to plant- and leaf-level farming. Leveraging AI to extract actionable insights from drone images, we provide growers with invaluable information about every square centimeter of their fields, across the entire season.

We collaborate with both growers, including agriholdings and farmers, cultivating corn seeds, as well as seed producers. Numerous top seed producers already entrust us with their detasseling efforts.

Don’t hesitate to reach out to us to address your detasseling challenges and stay ahead of the curve.

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90%+ precise plant distancing reports and gap detection in a few hours

90%+ precise plant distancing and gap detection report in a few hours with DJI Phantom 4 and Proofminder AI model

Understanding plant spacing and the number of missed plants is an important factor in successful crop and seed production. Plant population has a direct impact on the yield, quality, and health of the plants, as well as the overall size of the harvest. With the rise of technologies such as artificial intelligence and computer vision, the accessibility of drones and their widespread use in agriculture, it is now possible to implement trending precision seeding techniques to maximize yield and quality.

Impact of Plant Spacing

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.

Current agricultural practices to measure plant population and distance

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

Sowing zones are formulated based on the soil’s conditions. Conditions are either based on measurements (sampling or calculated from the electric resistance of the soil) or derived from past years’ vegetation health (satellite images and vegetation indexing);
For zones where the soil has higher capacity (water, nutrients), growers put more seeds, resulting in less distance between plants;
Figuring out the right zoning is a process that takes multiple years, which leads to more precise results and helps to obtain the best agronomic decisions and outcome;
Stand counting is key for zoning and precision seeding process/machine quality control.

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:

By analyzing data on a micro level, the platform sees and counts every single plant and is able to detect issues on the leaves;
We scan the whole field, so each cm2 of it is available to review on the screen or as a file to share, and can be used by a spraying drone or any Ag machine;
The Platform creates orthomosaics automatically, highlights problem zones and shows their GPS coordinates;
No specific knowledge of equipment is needed, we provide support and partner with drone service providers to cover the whole innovation process;
Quick innovation cycle and report building. It takes a few days for us to build a use case for a new plant type or a few hours to generate an actionable report for an existing use case;
The data can be used for other calculations on the same platform such as wildlife damage, disease recognition, yield estimation, identifying gopher holes, and many more.

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:

Exact number of plants
Exact distance between plants
Number of missing plants per coating
GPS coordinates of certain weeds
A visual overview of the field on a micro level
GPS coordinates of problem zones and missing plants
Additional insights and the possibility to upload or share the report or file.

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.

Contact us to try out our algorithm for precise plant spacing measurement and improve your production processes.

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High-precision plant stand count for corn, sunflower and sugar beet with a drone and AI

High-precise plant stand count for corn, sunflower and sugar beet by a drone and AI

Plant stand count is an essential task in yield management. It allows growers to estimate the plant population, density, germination rate, and plant health and make timely decisions that finally affect the yield. Common manual methods of plant stand counting have helped growers for decades. They are based on visual inspection and plant calculation on small pre-defined field areas. However, these methods are laborious and far from accurate. Fragmented plant stand count does not provide the complete picture, and problem areas with uneven emergence or weeds might be overlooked. The lack of information on the field eventually leads to a waste of resources and less profitable decisions.

New technologies like drones and AI leverage the opportunity to make Agri operations smarter and more efficient. With this innovative approach, growers can now receive accurate data, make timely decisions and sustainably maximise the yield. Surprisingly, this is not as complicated or costly as it might seem.

This article covers precise plant stand count using an off-the-shelf drone and Proofminder’s trained AI algorithm for accurate yield assessment and the following insights on the field. You will find practical tips on image collection and recommended approach for corn, sugar beet and sunflower, but the information is also useful for other field crops, vegetables and orchards. If you have a drone or considering buying one to turn a tedious task into an interactive process and get a high-precision result, keep reading. You will find drone requirements, flight tips and common mistakes, and learn how to get a precision stand count report in a few hours with an innovative AI farming platform.

Why and when do you need a precise plant stand count?

There are situations when a low accuracy report is acceptable, but it is absolutely essential to have a precise one if you aim to:

Check the sowing quality, especially if you are producing seeds;
Understand zones of varying productivity in the fields;
Receive accurate data during R&D projects;
Estimate the yield precisely in the early stages;
Spot rogues;
Make timely decisions, i.e., partially replant the field;
Increase the yield potential to meet the production goals.

When is the best time for plant stand count using a drone and AI?

Estimating the number of plants and their density is crucial for early-season yield management. The accurate information here is a chance to save the yield if something goes wrong and improve the harvest. To gather proper images for further analysis, consider the tips about plants and the weather.

The plant should be big enough to be seen from the air, but the leaves are not yet too close to each other to distinguish plants and estimate the density. As an example, for the precise stand count of corn, the plant should have about 3-7 leaves (V3-V7 vegetation stages). The weather should be stable during the footage, thus the lens can adapt to the conditions whether it is sunny or cloudy. Also, it should not be too windy, note that the wind speed may greatly vary depending on the altitude. Which altitude is right for a stand count? Find below!

Figure 1 – Corn field

Figure 2 – Manual plant stand count of corn

Capturing images by a drone – instructions and tips

The ideal resolution for plant stand count by a drone and intelligent software depends on the plant and the goal. For precise stand calculation of corn, sunflower, sugar beet, and some other field crops and vegetables would be 0.8 cm per pixel or less. What does it imply, and what kind of drone is suitable? The widely available DJI Phantom 4 Pro V.2. can be a good entry-level option for that job, similarly, the DJI Phantom 4 RTK is also a great option if you want a professional drone with high precision positioning. You will need to fly at 18-30 meter altitude to get the indicated resolution. Be aware that some of the Integrated controllers (the Plus versions) limit the flight altitude to 25m above the ground so if you want to count small crops and fly low, you would rather choose the simple controller and instruct the drone from your mobile or tablet. The ideal speed to capture detailed images would be between 3-5 m/s depending on the altitude and the wind conditions. Using this drone, you can proceed at about 25-30 hectares per day if you have enough batteries; mind you: you can charge them on the site. At Proofminder, we are working on novel ways to do this image capturing and foresee the possibility in the near future to capture up to double of this area per day by a Phantom 4 drone.

Figure 3 – Shooting images for plant stand count by DJI Phantom 4

Things to avoid; the Top-10 common mistakes in drone footage:

Wrong exposure setting, not properly assessing the weather, resulting in over- or underexposure. Overexposure is more of a problem than underexposure, so if you need to choose between cloud and sunny, and you are not sure, you can safely go for sunny.
Too much wind or unstable weather conditions result in blurry images.
Not equipped with sufficient memory cards, make sure you have at least a 64 GB card for ~40-50 hectares of land.
Not enough batteries and/or chargers to fly continuously during the day.
Shooting after rain may require some recalibrations because the plant on the wet soil may not be visible enough, keep this in mind.
Not flying with the right amount of front/side overlaps, potentially preventing stitching pictures together and creating an orthomosaic. 75% is a safe value in most cases.
Flying too fast results in blurry images.
No right logistics and setup – i.e., make sure you have a suitable car and path to access the field, have a generator available to produce power for all the equipment, battery charger and laptop, have a sun-shaded place to work from, etc.
No proper preparations in flight planning – e.g., cater for height differences in the field upfront.
Check the airspace before flying and make sure not to fly beyond visibility to avoid losing your drone.

Figure 4 – The process of drone footage for precise stand count

Figure 5 – The shape-file of the field

Plan stand count report and additional insights on your field

Following the instructions will result in lots of useful data and good images for further analysis and insights about the field and plants. What can you, as a grower, do with the collected images? There are a couple of ways – as an illustration, to analyse it manually, which is again time-consuming and subjective or use Artificial Intelligence, which can do the job quickly and accurately. The AI-powered platform can create an orthomosaic, an automatic plant stand count report and mark issues on the field that are not visible or not humanly possible to discover in traditional methods.

Images below show what your plant stand count can look like on the Proofminder platform.

On the automatic report generated in a system, you can see

Plant & row density;
Precise plant stand count;
Each plant is marked on the field with precise coordinates;
Plant distinguished by phenotype, in this case – male and female plants of hybrid corn are marked with a different colour;
Zoom-in feature to analyse specific zones, rows or plants.

Figure 6 – A plant stand count report on the Proofminder platform

Figure 7 – A plant-level view of a stand count report on the Proofminder platform

Additional insights & platform capabilities

During the corn plant stand count, we discovered that lots of plants on a field were destroyed by wild boars;
The problem areas can be marked with GPS;
Downloadable shapefile for further usage e.g., compare it with sowing facts;
As each plant has precise coordinates, derived metrics such as the distance of plants, density, gaps, row distance, etc. can be provided additionally;
Actionable insights on a level of leaf or plant.

Automated plant stand count - outcomes and benefits

The plant stand count accuracy of manual methods is hard to estimate; one thing is clear: it can only be precise on small analysed areas of the field; applying these numbers to the whole plot would not give precise information. Drones and AI technologies are able to provide growers with 90-99% of stand count accuracy and reveal other problems on the plant level.
Technologies make the plant stand count process way more precise, interactive and insightful.
Additional insights discovered: lots of plants have been destroyed by wild boars.
A helpful opportunity to export the stand count report and reuse its data in other farming activities.
Possibility to get the maximum out of the drone-made images and use this information for data-driven decisions and for growing more with confidence.

Author: Ksenia Korolkova

Variable-rate spot spraying with AI-generated map and traditional sprayer

Cost saving - variable-rate spot spraying with a traditional sprayer? Yes, it is possible with the help of Proofminder!

Let's talk about your project

Fill in the form and we will contact you shortly

Johnson’s grass and other weeds in corn: how to save up to 70% on chemicals

Johnson grass and other weeds in corn: how to save up to 70% on chemicals

Let's talk about your project

Fill in the form and we will contact you shortly

Detasseling: from manual labor, days on end – to drones and AI

Detasseling: from manual labor, days on end - to drones and AI

Why is detasseling crucial in hybrid seed corn production?

Challenges addressed by Proofminder

Let's talk about your project

Fill in the form and we will contact you shortly

90%+ precise plant distancing reports and gap detection in a few hours

90%+ precise plant distancing and gap detection report in a few hours with DJI Phantom 4 and Proofminder AI model

Impact of Plant Spacing

Current agricultural practices to measure plant population and distance

Let's talk about your project

Fill in the form and we will contact you shortly

High-precision plant stand count for corn, sunflower and sugar beet with a drone and AI

High-precise plant stand count for corn, sunflower and sugar beet by a drone and AI

Why and when do you need a precise plant stand count?

When is the best time for plant stand count using a drone and AI?

Capturing images by a drone – instructions and tips

Things to avoid; the Top-10 common mistakes in drone footage:

Plan stand count report and additional insights on your field

Additional insights & platform capabilities

Automated plant stand count - outcomes and benefits

Let's talk about your project

Fill in the form and we will contact you shortly

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