Skip to main content

4 posts tagged with "agriculture"

View All Tags

Autonomous tractors on market

· 7 min read
Mative CEO & Founder

What autonomous tractors are already on the market?

The majority of tractor manufacturers is working on the technology to let at least one of their tractor models work unmanned. A recap based on what we know now.

Just a handful of tractor types currently is available ex-factory as unmanned/autonomy ready. Backed by the OEM and not by a third party offering an autonomous retrofit kit. You can operate these tractors without a driver while an operator controls it from another tractor or other vehicle nearby, or even from a remote location. Japanese manufacturers Kubota and Yanmar were the first to commercially offer autonomous tractors in their country of origin, Japan. Followed by Monarch Tractor as well as by John Deere.

Upcoming developments

Every tractor manufacturer, whether they admit it or not, is developing driver optional tractors. Out in the open or behind closed doors. Vehicles that resemble the current looks of a tractor and built with components you’d normally find in tractors as we now know them for decades. Recent trade shows such as last year’s Agritechnica in Hanover, Germany showcased a few upcoming autonomous tractor types. Such as the Claas Xerion 12.590 TerraTrac, Fendt e107 Vario and 900-series and the Kubota AgriRobo MR 1000 A.

Earlier on, Case IH showed an autonomous Magnum 400 in Brazil and demonstrated an autonomous Magnum 340 in Austria. But also Belarus has made public that they are working on their autonomous tractor model A3523i. Other manufacturers that are very likely developing autonomous tractors include Farmtrac, Mahindra and also Keestrack. The latter might not be a very familiar name for most farmers and contractors. The manufacturer however has a long track record in construction machinery, owns tractor specialist Goldoni and manufactures Swiss Rigitrac developed electric tractors in its Goldoni factory in Italy.

Most autonomous tractors still have a cab

What commercially available autonomous tractors and those that are being developed have in common, is that most of the technology onboard can already be found in the latest tractor model series. Those are automated to such extent that they can automatically follow straight and curved AB-lines and contours, lower/lift and start/stop implements and machines and turn automatically on headlands due to sophisticated headland management systems. The only technology these tractors lack to work without a driver, are cameras and sensors to observe the surroundings, the machinery and the quality of the work being done.

Another aspect most available autonomous tractors and prototypes have in common, is the presence of a cab or at least a driver’s seat. This might be a result of a transition period between manned tractors and unmanned tractors similar to what we saw at Tesla and Beyond Meat for instance. The first Tesla cars still had cooler grills to resemble the familiar and generally accepted looks of existing cars and trucks. Beyond Meat and other vegan food producers first develop plant-based, vegan meat products that resemble looks, taste and texture of meat for initial acceptance, adoption and uptake. A further aspect comes down to the flexibility of driver optional tractors that include a cab, controls and an operator terminal. You can still use your tractor as usual, ánd you can drive it from one field to another on public roads. The latter might not be an issue on large outback farms, it is an issue in most other areas. Moving autonomous machinery from one field to another still requires a tractor or truck with a (flatbed) trailer.

Exceptions without a cab or driver’s seat by the way are the Belarus A3523i, the Kubota New Agri Concept and to some extent also the Krone/Lemken Combined Powers VTE.

Official statements

We asked most major manufacturers for official statements on the introduction of autonomous tractors and these are the answers we got.

Agco (Fendt) says: “We have several different projects for autonomy/autonomous solutions running at the same time but none of them are commercially available at this time.”

Claas says: “Our autonomy connect will not be available in 2024 but in the mid-term, without giving a concrete date.”

CNH says: “Currently we don’t have commercially available tractors that leave one of our factories capable of unmanned operations. Our Case IH Farmall 75C Electric and New Holland T4 Electric Power are capable of doing so and their prototypes are currently part of homologation procedures. Together with Raven, we can retrofit existing Case IH Magnum tractors with our autonomous retrofit kit in those countries where autonomous operations are legally permitted. This excludes Europe.”

Deutz-Fahr says: “In 2024 we will not introduce an autonomous tractor commercially.”

John Deere says: “Our wheeled and four-track model year 2025 8 series and 9 series tractors will offer an autonomy-ready option that will allow farmers to make the switch quickly and easily to fully autonomous operation when it’s right for their own farm. The autonomy-ready package offers all the hardware, software, and safety features that we know today will be required for autonomous operation in the future. The autonomy-ready package is available in the United States and Canada to order through local John Deere dealers. The only additional item a farmer will need to add in the future to complete autonomous operations will be the perception system. The perception system consists of cameras and vision processing units needed for autonomous operation.”

Trusted solutions and suppliers preferred

If you let your autonomous tractor work your fields, you trust your fields and your crops to something (rather) unknown. Lessons learned during last year’s experiments in the Netherlands with a common tractor retrofitted with an autonomous kit, include farmers’ clear opinions that they find ‘an unmanned autonomous tractor mainly interesting for soil preparation. This opinion primarily arises due to the need for supervision, both for safety and to ensure the machine operates effectively and maintains work quality. Crop health and yield determining operations such as planting and hoeing are easily trusted to autonomous alternatives as sufficient monitoring solutions still lack. A blocked planter row or a hoeing knife dragging along a piece of wood or metal and thus destroying a crop row, are the least farmers want. John Deere and others have a clear reason why they initiate autonomous operations with soil preparation tasks.

Then, let us draw another parallel with the automotive industry, at least in Europe. It took Korean car manufacturers decades to gain trust in their products. Not only from a quality and reliability point of view, but also from a depreciation or resale / trade in value point of view. While currently, again at least in Europe, it’s the Koreans along with Tesla who are known for their innovations, reliability and resale value for electric cars. While currently, the countless Chinese electric car makers are dealing with the same challenges, yet much and much faster.

The same is going in in agriculture. If you buy a tractor from one of the established manufacturers mentioned above, you know what you get. Quality wise, reliability and service wise, and you can also (roughly) estimate your resale or trade in value and thus your costs per hour. Also if you fitted it with an autonomous retrofit kit. That is not the case for field robots who are costly and for the time being, quickly depreciate/amortise.

Go to article source

Back to top

IoT & ML: Energy and economic benefits in an Agricultural Company

· 3 min read
Mative CEO & Founder

Introduction

Technological innovation is a fundamental lever for improving the efficiency and sustainability of agricultural companies. Mative, a leading company in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services capable of revolutionizing agricultural practices. This report explores the energy and economic benefits derived from adopting these technologies in an agricultural company.

Internet of Things (IoT) in Agriculture

Definition and Operation

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In agriculture, IoT sensors can monitor various parameters such as soil moisture, temperature, air quality, and crop conditions.

Energy Benefits

  1. Optimization of Irrigation: Soil moisture sensors allow for irrigation only when necessary, reducing water and energy waste used for pumping.
  2. Intelligent Management of Heating and Ventilation Systems: Temperature and humidity sensors help maintain optimal conditions for crops, minimizing unnecessary heating and ventilation use.
  3. Reduction of Energy Consumption of Agricultural Machinery: Tractors and other machines equipped with IoT sensors can operate more efficiently, reducing fuel consumption through optimized routes and operations.

Economic Benefits

  1. Increased Productivity: Continuous and precise monitoring of environmental conditions allows for timely interventions, improving crop health and yield.
  2. Reduction of Operating Costs: Optimization of resources such as water and energy leads to significant reductions in operating costs.
  3. Predictive Maintenance: Data collected from IoT sensors allows for predicting equipment failures, reducing downtime and repair costs.

Machine Learning (ML) in Agriculture

Definition and Operation

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In agriculture, ML can be used to analyze data collected from IoT devices, identifying patterns and trends.

Energy Benefits

  1. Advanced Weather Forecasting: ML algorithms can analyze weather data to predict climatic conditions, helping farmers plan irrigation and other activities more efficiently.
  2. Resource Optimization: ML can analyze historical data to suggest optimal use of resources such as fertilizers and pesticides, reducing environmental impact and energy consumption.

Economic Benefits

  1. Crop Management: Predictive data analysis allows for timely identification of diseases or infestations, reducing crop losses and improving product quality.
  2. Production Planning: ML can help predict market demand, optimizing production and reducing waste.
  3. Marketing and Sales: Market data analysis allows for optimizing sales and pricing strategies, improving the company's competitiveness.

IoT & ML: Implementation in an Agricultural Company

Consider a medium-sized agricultural company that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

  1. 30% Reduction in Water Consumption: Thanks to the optimization of irrigation based on real-time data.
  2. 25% Decrease in Energy Costs: Through more efficient management of heating, ventilation, and agricultural machinery.
  3. 20% Increase in Production: Due to better crop management and timely interventions against diseases and infestations.
  4. 15% Savings in Operating Costs: Thanks to predictive maintenance and optimized resource use.

Conclusion

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant turning point for agricultural companies seeking to improve their energy and economic efficiency. The benefits derived from implementing these technologies not only contribute to environmental sustainability but also increase the competitiveness and profitability of agricultural companies, positioning them well to face future industry challenges.

Agriculture: monitoring system

· 2 min read
Mative CEO & Founder

Warning and monitoring systems in Agriculture

Warning systems in agriculture play a fundamental role in monitoring environmental conditions, plant diseases, pests and adverse weather conditions. These systems provide farmers with timely and relevant information to make informed decisions regarding agricultural practices, improving productivity and reducing losses. In this paper, we will explore the software used to develop warning systems in agriculture and provide some examples of such systems.

Software for Warning Systems in Agriculture

1. OpenWeatherMap API

Description: OpenWeatherMap provides global weather data through an API that can be integrated into agricultural warning systems.

Main features:

  • Provides real-time weather data and long-term forecasts.
  • Includes detailed information such as temperature, humidity, wind speed, precipitation and more.
    Use: Integration with warning systems to alert farmers in case of adverse weather conditions.

2. Crop Disease Prediction using Machine Learning

Description: This software uses machine learning algorithms to predict the spread of plant diseases based on historical and current data.
Main features:

  • Analyze data relating to environmental conditions, crop type and previous disease attacks.
  • Identify patterns and correlations to predict the spread of plant diseases.
    Use: Alerts farmers in advance of potential plant disease outbreaks, allowing them to take preventative measures.

3. Agricultural Decision Support Systems (ADSS)

Description: ADSS are systems that integrate data from various sources to support agricultural decisions.
Main features:

  • They collect and analyze weather data, soil data, crop data and more.
  • They provide personalized recommendations to farmers based on local conditions.
    Use: Helps farmers plan agricultural operations efficiently and mitigate climate-related and plant disease risks.

Examples of Warning Systems in Agriculture

1. Early Warning System for Plant Diseases

Description: This system integrates weather data, crop data and predictive models to warn farmers in advance of the possible spread of plant diseases.
Functionality:

  • Constantly monitor environmental conditions and crop health.
  • It uses machine learning algorithms to predict the spread of specific diseases.
  • Send alerts to farmers via SMS or mobile app.
    Benefits: Reduces losses caused by plant diseases and optimizes the use of pesticides.

2. Weather Alert System for Precision Agriculture

Description: This system uses real-time weather data and forecast models to alert farmers

Agriculture: Smart Farming

· 3 min read
Mative CEO & Founder

Smart Farming Revolution: Software, Examples and New Ideas

Smart farming, or intelligent agriculture, is revolutionizing the agricultural sector through the innovative use of technology. This modern approach to farming uses advanced software, IoT (Internet of Things) sensors, data analytics and other technologies to optimize farming practices, improve productivity, reduce costs and mitigate environmental impacts. In this article, we will explore the key role of software in smart farming, provide examples of successful implementations, and discuss new ideas to further improve this agricultural revolution.

The Role of Software in Smart Farming

Software plays a vital role in smart farming, allowing farmers to collect, analyze and use real-time data to make informed decisions. The main software features in smart farming include:

1. Agricultural Data Management

  • Collection and storage of data from sensors, agricultural machinery and other sources
  • Efficiently organize and manage agricultural data to enable in-depth analysis

2. Monitoring of Crop Conditions

  • Using IoT sensors to monitor plant growth, soil quality, humidity and other key parameters
  • Data analysis to identify trends and anomalies in crop conditions

3. Resource Optimization

  • Optimize the use of water, fertilizers and pesticides through data-driven dosing systems
  • Monitoring energy consumption and optimizing the use of agricultural machinery

4. Forecasting and Planning

  • Using predictive models based on historical and current data to predict crop yields, plant diseases and other factors
  • Planning agricultural activities based on forecasts to maximize yields and reduce risks

Examples of Software-based Smart Farming

1. Precision Agriculture

Precision agriculture uses detailed data on soil and crop conditions to tailor agricultural practices in a targeted manner. Precision agriculture software integrates data from sensors, drones and satellite images to optimize irrigation, fertilization and disease management.

2. Livestock Management

Livestock management software allows farmers to monitor animal welfare, milk and meat production and health parameters in real time. These systems improve productivity and animal welfare through continuous monitoring and proactive management.

3. Monitoring of Plant Diseases

Plant disease monitoring systems use weather data, satellite imagery and image analysis to identify early signs of disease and pests in crops. This allows farmers to intervene quickly to prevent the spread of diseases and limit damage to crops.

New Ideas to Improve Smart Farming

1. Artificial Intelligence for the Diagnosis of Plant Diseases

Using artificial intelligence algorithms to analyze plant images could enable faster and more accurate diagnosis of diseases. This would help farmers make timely decisions on the use of plant protection treatments and crop management.

2. Blockchain for Food Traceability

Integrating blockchain technology into smart farming could ensure greater transparency and traceability throughout the entire food supply chain. This would allow consumers to access detailed information on the provenance and quality of food.

3. Vertical and Indoor Agriculture

The adoption of advanced software for vertical and indoor farming could revolutionize food production in urban areas. The use of automated environmental control systems and data analytics could enable the efficient cultivation of crops in limited spaces, reducing dependence on food imports.

Back to top