Smart agriculture system based on Big Data and AI
Host institution: International University of Casablanca
Location: Route provincial 3020, Casablanca 50169, Morocco
(1) Wilfried Yves Hamilton Adoni, Computer Science Professor, PhD Big Data and AI, email@example.com
(2) Hassan Aaya, Mechanical Engineering Professor, PhD
(3) Tarik Nahhal, Computer Science Professor, Phd Big Data Specialist
The intern will join the UIC staff of young staff who are working on a range of topics concerned with mathematics, computer science and its applications. They will mainly work on the topic below.
Proposed a conceptual design of smart system based on big data technology and artificial intelligence. The proposed system will be adapted for large scale climate data processing with commodity hardware.
Implement some machine learning and deep learning algorithms for weather prediction and give some insights to famers in order to take good decisions.
The main objects of the implemented algorithms are real-time collect of climate data, weather prediction, prediction of agricultural production based on climate data and soil type.
Project Expected Outcomes
Long-Term goals: Since the observational data in the region are often scarce and uncertain, the computation of the indices will be done using different meteorological data sets based on gauged and/or satellite precipitation and temperature data. In this sense, the establishment of a system capable of collecting a large amount of climate data will make it possible to participate in the actions of climate change.
Each country in Africa has a National Meteorological Service (NMS). The NMS provides their country with the short-term forecast, and also often provides a special service for the aviation sector. Many countries also provide a seasonal forecast. In addition, the NMS is usually the custodian of the long-term historical data for their country. Most countries claim their density of climatic stations is insufficient and the manual stations have recently been supplemented by sets of automatic stations. There are many important uses of these historical records, particularly with concerns about climate change, but most services would agree that their existing data are currently insufficiently analyzed.