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.

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 the 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 more physically uniform batch, an 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 reducing 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 effective 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.

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.

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!

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

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. Keep reading!

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.

Book a Demo to Learn More about Proofminder

Let's Do the Better Job for Your Farm This Season

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10+ efficient AI computer vision use cases for higher yield

There is a great need to improve crop productivity, and AI is doing it quickly and efficiently, not just in laboratories but on actual Agri holdings!

We have collected great examples of how intelligent algorithms help crop production and increase growers’ productivity during the season, from pre-planting to post-harvest. All the use cases listed require visual data (preferably from drones) and trained AI models to solve particular issues. In a nutshell, visual data provided by drones are the “eyes” of the operation, and AI and machine learning intelligence is the “brain,” together, they are changing the Agroindustry making it more productive and efficient.

This article covers AI application for crops and vegetables such as cereals, corn, soy, wheat, sugar beet, and more, but specific use cases for orchards, indoor farms, and livestock are also feasible and beneficial, this is just a topic for another discussion.

All is visible on the field during the season can be measured, estimated, and improved with the help of AI.

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Agriculture cycle

Crop planning

1- Waterlogging and field relief analysis.

These details most of the time cannot be precisely defined with the human eye or satellite images. Drone images and AI can provide farmers with insights that can bring value even before the first plants appear on the field. Knowing the field’s specificity will help support numerous upcoming decisions on seeding, irrigation, inputs, etc.

Planting

2 – Stand count and yield assessment.

Review the precise number of plants in a specific field and make decisive actions if it’s under the norm or expected results. Evaluate seed quality (germination rate), decide whether to replant or not, assess the yield, and mark the zones of potential yield losses.

3 – Plant density.

Since both high and low crop densities reduce yield and total revenue, it is crucial to know the situation on the field as soon as possible. Growers know there is a population for each crop that maximizes crop yield. Start strong!

Crop monitoring

4 – Heading date/flowering stage detection.

Accurate chemical thinning of some crops and fruit trees requires estimating their blooming intensity and determining the blooming peak date. With drone data, AI quantifies the size and number of flowers for timing fertilizer input.

5 – Weed detection.

Left uncontrolled, weeds can result in 100% yield loss. With vision processing and machine learning technology, today’s AI-driven equipment can reduce 80-90% of herbicide use. AI can distinguish and classify weeds with a high level of accuracy to support wise decisions.

6 – Disease detection.

Up to 50% of yield loss is caused by pests and diseases every year. AI technology helps diagnose plant diseases, classify them and significantly decrease the number of chemicals applied.

7 – Insect damage.

It is humanly impossible to distinguish insect categories and the growth period of insects without the knowledge of entomology. AI computer vision makes it possible to recognize and classify insects. It can quickly locate the information of an insect positioned in a complex background, accurately distinguish insect species with high similarity, and effectively identify the different phenotypes of the same insect species in different growth periods. This information will help reduce the number of insecticides applied and fight pests intelligently.

8 – Nitrogen analysis.

The deficiency of this macro-nutrient directly affects crop health and yield. However, when Nitrogen inputs to the soil system exceed crop needs, excessive amounts of nitrates enter ground or surface water, thus leading to significant environmental harm. With the multispectral drone camera and intelligent, trained algorithm, staying informed and supporting decision-making on fertilizer quantity and timing is possible.

9 – Crop monitoring and Plant stress analysis.

Tracking healthy leaf color, plant growth, and ever-changing environmental conditions during the season requires significant time and human efforts. AI computer vision offers an effective alternative for growth stage monitoring because of its low-cost (relative to person-hours invested in manual observations) and the requirement for minimal human intervention, as a result – less costly mistakes and better yield.

10 – Specific tasks and quality control.

As an example – missed tassel recognition during detasseling in maize seed production. Every tassel not removed by a machine leads to unwanted pollination and decreased genetic purity of maize seeds. AI can recognize missed tassels from 3 cm size before they start pollinating and help to save the whole field.

Harvest & Postharvest

11 – Plant size for harvest readiness.

In practice, the crop growth stage is still primarily monitored by-eye, which is laborious and time-consuming and subjective, and error-prone. The application of AI computer vision offers a precise, cheaper alternative to manual observations. It is now easier to plan the labor needs, predict the best harvest day, and even refine revenue projections.

12 – Remaining plant sizes and count.

Make sure that you do not leave the revenue behind. This second check and intelligent model will help identify the remaining plant counts and size. Last but not least – there are insights about quantifying crop residue, which help you with your further soil management strategies (like cover cropping or no-till planting).

Final word.

AI can provide growers with instant insights from their fields help them to identify areas that need fertilization, irrigation, or pesticide treatment. The AI technology combined with timely actions allows growers to reduce the number of fertilizers, help increase food production while minimizing resources, increase yield and profit, and make growers’ lives easier in the current labor-shortage situation. With valuable information, growers can make better decisions, recalibrate their future planting strategy, and finally grow more with confidence.

Artificial intelligence hunts ragweed or why Proofminder was awarded as a most innovative AgTech startup

Artificial intelligence hunts ragweed or why Proofminder was awarded as the most innovative AgTech startup

Budapest, 07/12/2021 - The Agtech Summit and demo-day at the end of November closed the 2021 innovation programme of the National Chamber of Agriculture. The main event of the day was the presentation of the startups participating in the program, in particular, the selection of the most innovative startup, which this year was awarded to Proofminder, the platform that helps to recognize crops and weeds with artificial intelligence. The company tested its solution in a three-month project with Corteva Agriscience, which allows for targeted ragweed eradication on thousands of hectares by analyzing drone footage.

The problem of weeding affects all plants and is causing serious damage to agricultural production. Ragweed is also extremely allergenic, with 77 million people projected to be affected in Europe by 2050. This is one of the great challenges facing the agroindustry and the food supply chain. Eradicating ragweed in the environment of other dicotyledonous plants, especially sunflowers, is a particular challenge. Although a new herbicide is now available thanks to Corteva’s new innovation, which allows effective control of ragweed in sunflower fields, accurate spatial identification of ragweed infection by air has not yet been resolved. Proofminder made a breakthrough in this area.

The platform allows you to determine the economically and environmentally ideal approach by identifying ragweed and other weeds. The results can be evaluated by specialists in a digital environment on a map and with the help of the system, they can make an accurate application plan for treatment with either a precision field sprayer or a drone.

“Special drone footage from the area is loaded into Proofminder’s system, which we’ve trained to detect and distinguish between useful plants and harmful weeds” – explained Norbert Havas, the company’s chief technology officer. “By identifying areas covered with ragweed, it calculates the degree of infection. During the project, we found that ragweed covered 71.45% of the area, which without intervention causes significant damage to the crop.” – he explained, breaking down the essence of the winning project.

The recognition of weeds provides an opportunity to estimate the weed cover of the areas and to determine the weed composition so that the same crop averages can be achieved with more environmentally friendly protection.

“We need to supply the growing population in ever-shrinking production areas. A key issue is to ensure that all crops receive maximum care with the least environmental impact for efficient and sustainable farming. Thanks to artificial intelligence, we now have the opportunity to set up a virtual farmer next to each sunflower.” – the motivation speech of the program manager of the National Chamber of Agriculture.

“On the one hand, the incubation program provided a unique opportunity for testing and, on the other hand, it showed us the direction in which we need to continue to develop over the next year and a half. ” – said Norbert Havas, adding “Our team is now looking for an investor to implement the product development plans and to introduce our solution to the wider Hungarian and international markets, and to expand the team to achieve these goals.”

About Proofminder

The company was founded at the beginning of 2021 by a team of three people with IT, business development and enterprise experience. The company has prepared its marketable product via bootstrapping without investor help. The first large corporate customers and partners have already started to use it in the 2021 agricultural season. The team is now looking for an investor and will use the capital to further develop their product and to carry out intensive go-to-market activities.

About NAK TechLab

NAK TechLab is the incubation program of the National Chamber of Agriculture, which aims to find and accelerate the growth of the most innovative solutions of the Hungarian agricultural sector and food industry, thus making industry and the market more competitive, sustainable and environmentally conscious.

About Corteva Agriscience

Corteva Agriscience™ is a publicly listed, global, purely agricultural company that offers producers around the world one of the most complete portfolios in the industry – including a balanced and varied combination of seed, crop protection and digital solutions – that focus on maximizing productivity to increase yield and profitability. Its best-known product brands in agriculture, as well as industry-leading product and technology research, define Corteva’s role in stimulating growth, so the company is committed to working with stakeholders throughout the food chain system and will do everything it can to improve the quality of life of producers and consumers, maintaining this process for generations to come.

Leveraging AI in Hybrid Seed Production

Case Study: Innovation in hybrid corn seed production

Innovation in hybrid corn seed production: Identifying missed tassels during detasseling to increase genetic purity, decrease cost and prevent revenue loss

Hybrid corn seed production problems

High chances of losing a field due to missed tassels in female inbred rows
High cost of manual detasseling due to reruns
The procedure must be carried out in a very limited time with the high accuracy

CHALLENGE

Machine detasseling only results in around 85% pulling vs the needed 99.7% for hybrid corn seeds.

Any tassel not removed during the detasseling phase of hybrid seed corn production can result in unwanted pollination, degrading the necessary genetic purity and quality of seeds thus impacting their commercial market value.

Drone images uploaded to Proofminder platform. We collected data from 5 distinct fields and multiple corn varieties
Field visualization. Orthomosaic of the plot is automatically created in the system
High precision stand count readily established
Male and female lines are distinguished based on phenology. Sowing structure, lines, distances between lines and individual plants identified without reliance on sowing or other external data. Male/female distinction allows to ‘ignore’ male tassels during the missed tassel identification phase
Clear report ready. Actionable insights in the palm of our hands
Yield saved. With timely actions taken by the agronomist, the cost of detasseling can be decreased by up to 1/3

DETAILS

Data Collection Approach:

Drone camera angle set at 60 degrees not at nadir – much larger plant area visible, training and verification is easier
Our algorithm calculates tassel location with 5-10cm accuracy based on drone location
Off-the-shelf drones (DJI Phantom, Matrice 210 v2 + Zenmuse X5S, etc.) and flight planning software (DJI Ground Station Pro) used
Above 100 hectares per drone per day coverage and processing possible

Identification of missed tassels:

Tassels categorized and predicted per size (S-XL)
Male tassels excluded, leveraging row identification algorithm
Identification is infinitely scalable using cloud resources – processing of 1 hectare on 1 node under 30 minutes
Identified missed tassels visualized in the Proofminder application on desktop/tablet/mobile but can be exported into geojson or other common geospatial formats

Implement the precise detasseling use case on your farm

Avoid losing a field due to missed tassels

Get higher genetic purity of the hybrid corn seeds

Decrease cost of detasseling up to 1/3 with less labor hours

TAKE CONTROL OF YOUR DETASSELING PROCESS

Book a demo to save the fields and improve seed quality on your farm

Book a Demo

Proofminder joins NVIDIA Inception

Proofminder x NVIDIA Inception

Proofminder Joins NVIDIA Inception

Budapest, Hungary — October 14, 2021— Proofminder announced it has joined NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data science.

Proofminder is focused on creating a platform that supports all stages of the AI lifecycle and allows customers to spend 70% fewer resources on the implementation of AI, perform experiments, measure the results, and implement the best visual intelligence use cases in practice. As an accessible computer vision platform, it aims to enable organizations and individuals in the Agroindustry to implement AI computer vision into their processes and transform their visual data into valuable decisive actions. Proofminder’s strategic goal is to create a reliable, affordable, and self-sufficient AI computer vision ecosystem that can radically increase the effectiveness of individuals, products, and organizations.

NVIDIA Inception will allow Proofminder to elevate the platform development process with access to industry-leading technologies. It will also ease the process of implementing AI computer vision in the Agroindustry and other niches where aerial and overall geospatial data is crucial. In addition, Inception offers Proofminder the opportunity to collaborate with industry-leading experts and other AI-driven organizations.

“NVIDIA’s revolutionary parallel computing technology enables our product to process and analyze images from thousands of hectares daily and deliver complex insights using leaf-level vegetation information.” – Norbert Havas, Proofminder CTO.

NVIDIA Inception helps startups during critical stages of product development, prototyping, and deployment. Every NVIDIA Inception member gets a custom set of ongoing benefits, such as NVIDIA Deep Learning Institute credits, marketing support and technology assistance, which provides startups with the fundamental tools to help them grow.

About Proofminder

Proofminder is a high-fidelity aerial AI computer vision platform focused on the Agroindustry. We are making the power of computer vision accessible to growers, drone service providers, and other industry players. The use cases created with our platform help growers oversee millions of hectares, prevent losses caused by weeds or plant diseases, overcome the impact of climate change, make data-driven decisions and increase profit while saving resources and taking care of the environment. For manufacturers, service and technology providers in the Agroindustry, it’s a great opportunity to extend their services and follow sustainability trends.

For more information:

Norbert Havas

norbert.havas@proofminder.com

Case Study: Ragweed Identification

Ragweed (Ambrosia) identification based on phenology, using high-resolution RGB drone aerial photos

CHALLENGE

Ragweed pollen is a common allergen causing yearly suffering for millions of people. A single ragweed plant can produce up to a billion pollen grains in a season, which are carried long distances by the wind. It is a major health concern in many parts of the world – also, various countries have laws imposing fines if too much ragweed is found on a property.

SOLUTION

Our partner turned to Proofminder to be able to recognize and identify ragweed on ragweed on thousands of hectares of land daily, based on plant phenology.

It only took a few weeks for visual AI development and deployment on the Proofminder platform to ensure scalability.

Ragweed detection and the calculation of infection metrics became accessible via a simple, browser-based map display, to take decisive actions.

DETAILS

Orthomosaic is automatically created
Multiple Machine Learning algorithms were evaluated by leveraging Proofminder’s MLOps quick iteration capabilities. SVM and Random Forest were deemed most applicable
Infection metrics are accurately calculated according to local law
Country-level deployment envisioned using Proofminder:

1st pass using satellite photos to identify potentially infected areas
2nd pass uses drone photos for detailed analysis and ragweed identification
3rd pass can focus on elimination – e.g. spraying

Why Proofminder

Why we’ve created Proofminder and why we believe it will change the Agroindustry

If not now, when?

Each year, plant diseases cost the global economy around $220 billion and invasive insects around US$70 billion. Yield losses in crops due to weeds depend on several factors such as weed emergence time, weed density, type of weeds, and crops, etc. Left uncontrolled, weeds can result in 100% yield loss. Add here other Agroindustry problems such as labor shortage and lack of experts, fertilizer overdosing leading to environmental problems and wasted budgets, and a high level of uncertainty in harvest forecasting due to climate change and soil degradation, and you will see the many ways the industry could improve.

Agribusinesses are losing a significant part of their revenue while the world is pressing them to build a reliable and self-sufficient food-production system. Working for quite a while with agribusinesses and data we deeply understand how hard it is to make the right decisions, even when you have tons of information – the insights on the data are not always actionable; impact is not measurable and cannot be deployed at scale.

To address these challenges, growers are now integrating precision agriculture solutions into their processes (e.g., to forecast yield, eradicate weeds, and monitor crop health). But mainstream precision agriculture concentrates on fields and zoning, mainly based on satellite imagery and cannot address many agriculture challenges. As an example, current solutions for precision farming like digital scouting or NDVI provide inefficient or low–quality data, that are hard to interpret or not sufficient to make wise agronomic decisions.

We are changing the game

Proofminder brings precision agriculture to the next level by enabling growers to deploy Agro-specific AI models into their processes with the click of a button. The quick innovation cycle allows the creation of new use cases in days, e.g. hyper-precise plant stand count and distancing, weed detection, missed tassel identification in hybrid seed production, wildlife damage and more. Proofminder makes it possible to seamlessly implement complex AI-powered precision agriculture solutions into a farm or food production company of any size. Our independent, powerful, affordable ecosystem aims to radically increase the effectiveness of individuals, products, and organizations across the Agroindustry.

Proofminder extracts insights from drone images by AI to provide growers with valuable information about every cm2 of field across the season.

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We make it dramatically easy to start using AI without any technical knowledge, collaborate with other market players, exchange data, get actionable insights from the visual data and transform them into valuable determining actions.

Who Can Benefit From It?

Growers. Field crop and seed producers, vegetable and root growers, tree and orchard farm managers now can implement our Agro-specific AI models into their processes with the click of a button. Proofminder repository is holding AI solutions that are solving specific challenges for growers that require leaf-level imaging data and AI to work: e.g., weed detection at scale, missed tassel identification in hybrid corn production, wildlife damage and more. Some AI Solutions are available to growers right away to show the real impact, and we are open to new ideas and developing new use cases each week, thanks to our quick innovation cycle. Growers can deploy those AI models to their production processes without any technical knowledge. The output of the AI models shows on an interactive map in a friendly user interface.

Drone service providers & Agro equipment manufacturers. Our platform helps with core pain points – it helps to find new clients and extend the services, increase the product capabilities and its life-cycle and show the real value and necessity of having a drone or other Agro equipment on the farm these days. For instance, we are partnering with drone service providers and have a partnership program for individual drone pilots and companies.

We are providing a scalable cloud solution to handle the required data volume.

Other Agroindustry players. We make it easy and seamless to gather precise agriculture data on the platform, process, share and realize value on it in a bi-directional fashion between growers and agroindustry players without lock-in to any large player’s ecosystem. The ability to collaborate around this data could be useful for Agri-Insurance or financial organizations, government needs and more.

What’s next?

Our ecosystem is growing fast, new industry players, innovators, drone service providers and growers from all over the globe join our platform every day to increase revenue, scale their expertise and get the most ROI on every hectare.

Book a demo to join the Proofminder ecosystem. Let’s drive those positive Agroindustry changes together!

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

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

Why is detasseling crucial in hybrid seed corn production?

Challenges addressed by Proofminder

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

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

Book a Demo to Learn More about Proofminder

10+ efficient AI computer vision use cases for higher yield

There is a great need to improve crop productivity, and AI is doing it quickly and efficiently, not just in laboratories but on actual Agri holdings!

Agriculture cycle

Crop planning

1- Waterlogging and field relief analysis.

Planting

2 – Stand count and yield assessment.

3 – Plant density.

Crop monitoring

4 – Heading date/flowering stage detection.

5 – Weed detection.

6 – Disease detection.

7 – Insect damage.

8 – Nitrogen analysis.

9 – Crop monitoring and Plant stress analysis.

10 – Specific tasks and quality control.

Harvest & Postharvest

11 – Plant size for harvest readiness.

12 – Remaining plant sizes and count.

Artificial intelligence hunts ragweed or why Proofminder was awarded as the most innovative AgTech startup

Case Study: Innovation in hybrid corn seed production

Innovation in hybrid corn seed production: Identifying missed tassels during detasseling to increase genetic purity, decrease cost and prevent revenue loss

CHALLENGE

SOLUTION

DETAILS

Implement the precise detasseling use case on your farm

TAKE CONTROL OF YOUR DETASSELING PROCESS

Proofminder x NVIDIA Inception

Proofminder Joins NVIDIA Inception

Case Study: Ragweed Identification​

Ragweed (Ambrosia) identification based on phenology, using high-resolution RGB drone aerial photos

Why we’ve created Proofminder and why we believe it will change the Agroindustry

If not now, when?

We are changing the game

Who Can Benefit From It?

What’s next?

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Case Study: Ragweed Identification