Data science and statistics in agriculture and horticulture

Our strength lies in developing models that transform data from experiments, sensors and genetic research into reliable insights. Wageningen University & Research combines statistics, data science, AI and domain expertise in multidisciplinary teams. This enables us to predict crop yields, detect pests and diseases at an early stage, and accelerate breeding programmes.

What sets us apart is our methodological depth: from statistical genetics and data integration to explainable AI. This ensures not only accurate analyses, but also transparency and confidence in the results. The outcomes of our work form the building blocks for applications in AI and simulations, and are translated into decision support systems that provide user-friendly recommendations for companies, breeders and policymakers.

• Statistical modelling and predictions: WUR develops advanced statistical models that enable reliable predictions across a wide range of agricultural and horticultural applications. These models help businesses and growers make better-informed decisions, for instance in plant breeding or when assessing risks related to exposure to chemical substances.
• Explainable AI and decision support: Our AI systems are not only powerful but also transparent. We explain why a model generates a particular recommendation and which datasets were used, so that users can understand and trust the outcome. This also makes it easier to anticipate how new datasets may contribute to the result.
• Statistical genetics and breeding: Using tools such as the StatGen Pipeline, we link DNA data to phenotypes. This accelerates breeding programmes and supports companies in developing new crop varieties.
• Data integration and scalable solutions: We combine datasets from research, field measurements and monitoring. This results in scalable applications that can be used across different production systems.