Olive Trees Health and Yield Prediction through EO data and Machine Learning

The olive tree (Olea Europaea) is native across the Mediterranean region. It is among the oldest fruit trees cultivated in north African countries. In Morocco alone, it occupies 65% of the national arboricultural area with a production exceeding 1.4 Tons between 2016 and 2019, creating more than 50 million workdays. However, this cultivation faces hardship ahead, mainly because of climate change and water deficiency, hence the urgent need to take rapid action to enable high-yield, high-quality, sustainable, and resilient production. This study aims to assess the olive trees’ health and predict their yield using EO data of different sensors including Sentinel (1 and 2), Landsat, Mohamed VI satellite imagery and Unmanned Aerial Vehicle. The EO data will be combined with climatic data and Machine Learning models. The main objective is to develop an open-source EO workflow that will be applied to other regions of Africa and be helpful in monitoring tree health and early forecasting of olive production. The developed workflow could be used by different types of end-users such as governmental institutions, researcher institutions as well as farmers for deriving the information at national, regional or local scale respectively.