Mycotoxin contamination in small grain cereals can lead to safety incidents, animal and human health problems and economic losses. Mycotoxin production is largely influenced by weather conditions during critical crop growing stages (i.e. flowering and harvesting) and agricultural management practices (i.e. soil tillage, cultivar, previous crop, fungicide application, etc).
Due to the temperature increase and changing precipitation pattern changes caused by climate change, mycotoxin contamination in food and feed crops has become one of the top threats worldwide for human and animal health. A reliable precise mycotoxin early warning system is therefore needed to ensure food and feed safety without polluting the environment and to achieve a sustainable agriculture.
The proposed project aims to predict on-site mycotoxin contamination in cereal grains in the Netherlands at early crop growing stage using new technologies like hyperspectral imaging (HSI) and machine learning. Such a site-specific crop management system will help farmers to do the right thing in the right place, in the right way, at the right time. For instance, based on predictions of the presence of mycotoxin producing fungi, farmers can use fungicides in the right dose exactly at places in the field where it is needed. This multi-disciplinary project will integrate a variety of data, farm expert knowledge, technologies and algorithms with a dynamic consortium of farmers, farm cooperation, collectors, software developers, and researchers on phenotyping, precision farming and food safety. Ultimately, the proposed work will further improve and validate the existing mycotoxin prediction model DON-Control Tarwe with more detailed field monitoring data, optimize the application of hyper/multi-spectral imaging technique in the field condition, and explore the possibility of Fusarium spp. early detection, in order to increase the fungicide use efficiency and limit mycotoxin contamination in the Netherlands.