Agriculture is an extremely complex technological environment
There are hundreds of conditions which influence the efficiency of cultivations. Through centuries it was enough to get along with the experience of previous generations to do business as usual. Today there are more hectic parameters and a dynamically growing demand to bring the most out of an acre of field.
Our hybrid frame enables farmers to steer the cultivation process in a sustainable way with the latest technology using of IoT, AI, Big Data. Our sensors and drones combined with an agile software lead to crop more and better.
Collected information and regular monitoring are the basis for sustainable and optimized fertilization planning, weed control, germination rate control, crop estimation, phytosanitary mapping, disease monitoring, watering optimization, water supply monitoring and optimization of harvest time.
Of course, the knowledge of meteorological fact data is only one of the factors – albeit very important – of the information. At the same time, very accurate data for each area can be spatially local. For a particular table, we can know exactly everything but the relationships within the board can only be determined if we can visually check the area.One of the key objectives of an IT-supported agricultural project is a highly automated drone-based survey and monitoring system that allows monitoring and mapping of the diversity of nutrients and water supply problems for better and optimal nutrient management and irrigation.
The basis for a weed survey system is developing for sustainable chemical applications (potentially air application). Providing accurate and very high input and background data for the agricultural inventory system, which is also the basis for area-based subsidies. Combined information, combined with weather data, can improve plant health observation accuracy, disease prediction, and baseline for station and sensor data extraction.
Previous tests demonstrate the successful application of harvest optimisation technology, which is key to higher quality and quantity yields.The implemented system, as well as the collected and derived data can be integrated into an integrated database of land use, climate change, yields and other information, and a source of BigData analysis using artificial intelligence-based learning algorithms that helps sustainable and optimised agricultural decision-making.