Smart farming and agricultural technology: monitoring systems, autonomous tractors, telematics and data for farms.
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Advanced telematics for agriculture means reading CAN bus and GPS data from tractors, harvesters and other farm vehicles to know in real time where they are, how they work and how much fuel they use. Mative's Synapsis FMX Smart CAN Control does this with the Teltonika FMB140 device and the Synapsis IoT platform, adding alerts, geofencing and maintenance scheduling based on actual use.
Only a handful of tractors are available ex-factory as autonomy-ready: Japanese manufacturers Kubota and Yanmar were the first to offer autonomous tractors commercially in Japan, followed by Monarch Tractor and John Deere. Most other manufacturers are developing driver-optional models, and most autonomous tractors and prototypes still have a cab, so they can be used as usual and driven between fields on public roads.
IoT and machine learning help an agricultural company save water, energy and money: soil moisture sensors allow irrigation only when needed, temperature and humidity sensors cut unnecessary heating and ventilation, and ML models support weather forecasting, crop management and production planning. For a medium-sized agricultural company, Mative's forecast analysis after one year points to 30% less water consumption, 25% lower energy costs, 20% more production and 15% savings in operating costs.
Warning systems in agriculture monitor environmental conditions, plant diseases, pests and adverse weather, and alert farmers in time to make informed decisions, improve productivity and reduce losses. They are typically built on weather data APIs such as OpenWeatherMap, machine learning models that predict the spread of plant diseases, and Agricultural Decision Support Systems (ADSS) that turn field data into recommendations.
Smart farming uses software, IoT sensors and data analytics to optimize farming practices, improve productivity, reduce costs and mitigate environmental impacts. Software collects and analyzes real-time data from sensors and machinery to manage farm data, monitor crop conditions, optimize the use of water, fertilizers and pesticides, and forecast yields and plant diseases.