The primary goal of PA is to optimize yield level and quality while preserving the natural resources to bring both an economic and and environmental benefit. Therefore, PA requires time- and location-based information on crop and soil conditions, which is later used in the agronomic decision-making processes.
Our team of researchers and research technicians at CPA works on many aspects of PA, amongst others on novel technology for remote sensing of plant properties, the automation of agricultural processes and the dissemination of PA data and solutions to the farmers.
Our research in cereal crops, berries, grassland, and potatoes delivers methods for obtaining data on plant parameters such as nitrogen content, water status and pest infestations. We set up methods and statistical models that can be used to estimate yield level and quality early in the season. In addition, we perform research on the automation of agricultural processes to turn the information into action.
The basic hypothesis of precision agriculture is that the optimum rates of inputs, such as fertilizer, vary situational and spatially within a field.
Integrated pest management (IPM) aims to keep pest populations below the economic injury level, where the term pest is here broadly meant, comprising insects, plant pathogens and weeds.
Advanced agricultural technology
The number and variety of sensors and other technological advances, which are usable for agricultural purposes are increasing rapidly.
Robotization and automation
Since 2014, we have utilized autonomous robots for increased efficiency in measuring campaigns in our field trials, related to several projects.
Spatio-temporal data handling and multivariate statistics
Data useful for precision decisions and operations are commonly spatio-temporal of nature. Our data are generally multivariate data, typical for multi- and hyperspectral instruments.