Analysis and processing of data on plant growth in the field.
Planning and regulation of production to increase the harvest.
By Benjamin Nigl, Senior Account Manager
Today, it already controls vehicles, draws works of art and recognizes diseases: Artificial intelligence (AI) is a true multi-talent and offers great potential for future innovations, also in agriculture.
Between climate change and feeding the world: The world's farmers take on the vital task of supplying food to the world's ever-growing population. In this context, they face daily challenges such as the effects of climate change, rising commodity prices, or improving animal welfare, while they have to make their own production sustainable. There are many needs and parameters to consider in ecological, economic, and legal decision making. Within this field of tension, so-called digital agriculture can play a decisive role. In general, the topic of digitalization is nothing new for agriculture. Digitized machines and information systems have been around for a long time in the agricultural sector, often with fully automated functions or complex assistance functions for farmers. AI is also already being used on the modern farm. For example, to control complex process parameters in harvesting machines or for the fully automated operation of cleaning and feeding robots in barns.
The technological progress made so far and still to come in the field of hardware and software will open even more application scenarios and possibilities for using AI in agriculture in the future. In this blog post, we would like to present two possible applications.
Predicting extreme weather situations - reliably and region-specific
The so-called geodata are extremely important to be able to estimate when and where extreme weather situations, such as hurricanes, dry periods, heavy rain, or hail, will occur. To minimize potential damage to agriculture, such as soil erosion or crop losses, a simple estimation of such dangers is not sufficient. Rather, a precise and region-specific forecast of emerging extreme weather situations is required.
Such forecasts can become reality with the use of AI in agriculture. The increasingly extensive geodata, i.e., spatiotemporal satellite and weather data, must be made analyzable and processable for this purpose in the form of a geodata infrastructure. This data infrastructure can then be linked to relevant agrometeorological parameters such as soil moisture, daily precipitation, or soil moisture. This results in dynamic agricultural weather indicators which can be used to characterize any area of the world in terms of its historical and current weather situation. To turn this into a predictive artificial intelligence, the AI needs to be trained accordingly. This means linking the existing monitoring data (crop yields) and standardizing them in a common database.
The final step is to select and apply appropriate AI methods. This allows the identification of optimized agricultural weather indicators adapted to local site conditions. The resulting model finally allows region-specific spatiotemporal forecasts of extreme weather situations to be made.
Farming 4.0 - Autonomous agricultural machines
Hardly imaginable just a few years ago, today the first fully autonomous machines are already on the road throughout the world's fields. The number of these machines will increase dramatically in the coming years, as modern agricultural technology is not only indispensable in all areas of conventional and organic agriculture, but also offers great potential for saving time and resources. An American tractor manufacturer is already providing technologies for sustainable management of nutrient cycles, thus supporting soil-conserving forms of agriculture. With a new type of seed planter, the manufacturer is offering a machine that can plant seeds at speeds of up to 16 km/h with an accuracy of up to a few centimeters. During subsequent weed control, the chipper is controlled by high-speed camera and AI without injuring the crop plants.
In general, the possibilities offered by using AI in agriculture are endless. Technological progress will produce a number of innovations in the field of agriculture in the coming years - especially when you consider what is already possible at this point in time.
Autonomous robotic weeder and cultivator working in a farm field containing a young cruciferous vegetable crop. The weeder uses a combination of computer vision and AI to distinguish weeds from desired plants.
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