Tomato ripe check

10+ efficient AI computer vision use cases for higher yield

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.

Agriculture cycle

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.   

Definition of production zones


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! 

Precise plant standcount

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.   

Weed detection in sunflower

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.  

Tomato ripe check

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. 

AI use cases in Agroindustry

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Proofminder awarded as most innovative AgTech Startup

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. 

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Detasseling map of hybrid corn field

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


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.


  1. Drone images uploaded to Proofminder platform. We collected data from 5 distinct fields and multiple corn varieties
  2. Field visualization. Orthomosaic of the plot is automatically created in the system
  3. High precision stand count readily established
  4. 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
  5. Clear report ready. Actionable insights in the palm of our hands
  6. Yield saved. With timely actions taken by the agronomist, the cost of detasseling can be decreased by up to 1/3


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

Take control of the detasseling process and stay a reliable seed supplier

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

Get better yield this season

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Proofminder joined NVIDIA Inception Program

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  

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Case Study: Ragweed Identification​

Case Study: Ragweed Identification​

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


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.



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.


  1. Orthomosaic is automatically created
  2. Multiple Machine Learning algorithms were evaluated by leveraging Proofminder’s MLOps quick iteration capabilities. SVM and Random Forest were deemed most applicable
  3. Infection metrics are accurately calculated according to local law
  4. 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

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Ragweed detection map

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 yielderadicate 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 lowquality 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. 

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.   

Contact us to learn more and let’s drive positive changes in the Agroindustry together.

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