Olive tree health monitoring approach using satellite images and based on Artificial Intelligence: Satellite image for Olive tree health monitoring

Abstract

In Tunisian agriculture, olive tree cultivation plays an important role. It is affected by different stresses that jeopardize its sustainability. In this context, our objective is to enhance the resilience of this crop. To achieve this goal, our work consists of detecting anomalies at early stage starting from the tree to the field scale. The proposed solution takes advantage of the emergence of satellites with high spatial and temporal resolution. In particular, the Sentinel-2 sensor which is well-adapted to monitor the vegetation. It is characterized by ten spectral bands allowing to access to key vegetation properties such as leaf area index (LAI), chlorophyll content (Cab) and water content (Cw), etc. Direct estimation of these parameters for the image is not practical as the signal is convolved. For that, we use artificial intelligence techniques to separate the effects of the different properties. We develop an Artificial Neural Network (ANN) that learn to estimate the vegetation properties given the pixel signature. The learning is done using a database of simulated data produced by a radiative transfer model that simulates the satellite image given the vegetation cover properties. The stress detection using threshold on tree LAI and Cab. Comparison with ground truth with healthy and stressed plots has shown the validity of our approach.