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!

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.

Tweet

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.

Category: Uncategorized

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

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

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

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!

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.

Search

Recent Posts

Recent Comments

Stay with us