UM6P CEDOC- College of Sustainable Agriculture and Environmental Science: Crop mapping using - Benguerir

Yield gaps analysis is a powerful method to evaluate the spread between the actual yield and the attainable yield of a given crop in an area, region, or country.  It helps decision making on research, policies, development, and investment that is needed to improve yield. However, the agriculture data needed at national, regional, or field level in developing countries are often unreliable or not available, particularly for smallholder landscapes, which makes it difficult to measure yield gaps, identify the causes, and suggest and evaluate measures to improve productivity.

UM6P is initiating a research project, named “Imaging4Agriculture” in collaboration with CESBIO (Toulouse-France) which aims to develop a methodological approach based on more transparent, scientifically robust, and reproducible methods and tools for yield gaps analysis at different spatial scales, combining ground data, crop growth models, and a bottom-up approach to upscale results from location to region and country.

The project team will be made up of 11 professors of  UM6P and of 12 doctoral and Post doctoral students. The project will focus on Morocco and Senegal. The successful candidate will integrate the project team and will be part of the A-Lab (UM6P – Technopolis Rabat). He/She will work on developing and/or improving crop mapping technics based on satellite images and in particular hyperspectral images.

Key duties:

The PhD candidate will:

1. Take part in scientific research within the research project that will culminate in a doctoral thesis.
2. Engage in relevant research activities (e.g., workshops, conferences) in and out of the UM6P ecosystem (ALAB, CRSA, etc…).
3. Disseminate results of the doctoral thesis as papers through high impact peer-reviewed journals, conferences presentations and social media engagements.

The research activities of the student will include:

1. State of the art analysis
2. Set up data collection plans and participate to data collection in order to insure quantity and quality of datasets both in Morocco and Senegal.

1.  Improve crop mapping algorithms robustness (crop identification and area delimitation) based on time series relatively to satellite images times series regularity, mixed culture, use of meteorological data.
2.  Develop early crop mapping algorithms (the identification and yield estimation should be made as soon as possible if it is to be used by national authorities) in particular based on hyperspectral imaging.

Criteria of the candidate:   

The candidate should have:

1. A master’s degree in any of the following areas: Image processing, Artificial Intelligence, Data Science, Remote Sensing.
2. Programming skills and use various analysis tools such as R, Python, MATLAB, SPSS, and CDO is vital.
3. Evidence of publishing peer reviewed papers will be an added advantage.

Skills:   

1. Self-motivated with an interest in Artificial intelligence, datascience, remote sensing and agriculture.
2. Ability to work autonomously as well as work in a diverse team is an added advantage.
3. Strong communication and organizational skills are highly desired.
4. Very good level in english as a spoken and written language.