The company's press service highlighted the sophisticated approach that leverages artificial intelligence to optimize agricultural practices and maximize yields.
Data-Driven Agricultural Insights
Kernel has invested years in developing a comprehensive database for each field area, encompassing agrochemical properties, soil conditions, and various operational metrics captured by field equipment. This extensive data collection forms the backbone of their AI-driven productivity assessment model.
"We have developed our own database for each area over the years. We collected information about agrochemistry and soils, as well as a number of indicators that are monitored by the equipment during field work," the report said.
AI-Powered Productivity Model
The machine learning model developed by Kernel evaluates multiple factors influencing yield on a per-hectare basis. This AI-driven approach identifies the strengths and weaknesses of each field, enabling the creation of tailored algorithms to unlock hidden potential and enhance efficiency.
"This application of AI technology allows us to better understand the needs of each plot and, for example, to rationalize the nutrition and fertilization system," Kernel stated.
Precision Agriculture
By adjusting fertilizer rates according to the specific needs of each field, Kernel aims to maximize yields while minimizing costs, especially in fields with lower productivity potential. The company plans to conduct a detailed assessment post-harvest to evaluate the effectiveness of this AI-guided approach.
"Fertilizer rates were adjusted for each field this year to maximize yields and avoid the risk of increased costs in low-potential fields. And at the end of the harvest, a detailed assessment of the effectiveness of this approach in practice will be carried out," the report added.
Future Enhancements
Kernel's specialists are already working on refining the AI model to provide even more granular insights. The goal is to calculate productivity potential not just for entire fields, but at the level of homogeneous areas within fields. This detailed analysis will enable more accurate yield forecasting and resource optimization, while preventing soil overloading.
"Our specialists are already working to calculate the potential of not only the entire field, but to detail the data at the level of typical homogeneous areas. This will make it possible to more accurately analyze the variability of conditions within one field in order to more effectively forecast yields in the future, optimize resources and not overload the soil," the report said.
Conclusion
Kernel's adoption of AI technologies marks a significant step forward in precision agriculture, allowing for tailored interventions that enhance field productivity and sustainability. By leveraging detailed data and machine learning, Kernel is poised to optimize agricultural practices, reduce costs, and improve yields, thereby setting a benchmark in the agricultural industry.
