Agricultural fields are historically often managed uniformly, for example when it comes to fertilization. Nevertheless, significant variation in soil properties between and within fields does exist. Not taking into account this variability in field management can create a mismatch between the treatment and the actual needs of the crop. To address this issue, attention is shifting to precision agriculture practices. Nowadays, there are several techniques to map variability in soil properties (for example soils scans or drone flights), but often the costs associated with these methods form a barrier for farmers to start implementing them. Remote sensing with satellite imagery forms a cost-efficient option for farmers, as satellite images are freely available in Flanders. However, translating the satellite data and derived parameters into understandable, quantitative and actionable knowledge remains a challenge.

This research wants to leverage the observed spatiotemporal patterns to provide insight in inter- and intra-field variability. By using this information on historical field variability, zones of suboptimal soil fertility will be identified and diagnosed, so that farmers can optimize their land management practices. To this end, we will

-          Screen for soil variability through timeseries analysis of available satellite, weather and soil data

-          Develop a decision support tool to identify zones/fields that need attention in terms of soil fertility

-          Combine agronomic knowledge and remote and proximal sensing data into a diagnostic AI tool for identifying the cause of suboptimal soil fertility 

We are looking for a highly motivated PhD candidate to join our team. Do you hold a MSc degree in Engineering (bioscience, agricultural) or in Science (earth and environmental, agriculture, data science) and do you match with the following requirements? Then you may be the colleague we are looking for!

-          Strong interest in remote sensing and AI for precision agriculture applications (experience with satellite data and/or machine learning is a plus).

-          Head in the ‘cloud’, feet on the ground: analytical mind that loves digging into (satellite) data, while still keeping touch with what the data actually mean on the field.

-          Programming skills for implementing the developed tools, as we want to bring our research to the daily practice of farmers (experience with Python and openEO is a plus).

-          Pro-active communicator that reaches out and seeks feedback (this PhD research is part of a larger project with multiple partners).

-          Independent worker that shows initiative, takes ownership of the work, respects agreements and deadlines.

-          Fluent written and oral communication in English.

-          We offer a full-time PhD position for 1 year. After positive evaluation, the contract can be extended to 4 years.  

-          Excellent guidance by our young, dynamic and multidisciplinary team

-          State of the art research infrastructure.

-          A challenging job in a young, dynamic environment.

-          High level scientific training at a top-ranked university.

-          Being part of a world-class research group.

-          Remuneration according to the KU Leuven salary scales: https://www.kuleuven.be/personeel/jobsite/en/phd-info

For more information please contact Prof. dr. ir. Wouter Saeys, tel.: +32 16 32 85 27, mail: wouter.saeys@kuleuven.be or Mrs. Annelies Postelmans, tel.: +32 16 37 64 31, mail: annelies.postelmans@kuleuven.be.