Fouad Yacef

Faiza Mekhalfa, Fouad Yacef

Computer vision techniques have attracted a great interest in precision agriculture, recently. The common goal of all computer vision-based precision agriculture tasks is to detect the objects of interest (e.g., crop, weed) and discriminating them from the background. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight, causing losses to crop yields. Weed detection and mapping is critical for site-specific weed management to reduce the cost of labor and impact of herbicides. This paper investigates the use of color and texture features for discrimination of Soybean crops and weeds. Feature extraction methods including two color spaces (RGB, HSV), gray level Co-occurrence matrix (GLCM), and Local Binary Pattern (LBP) are used to train the Support Vector Machine (SVM) classifier. The experiment was carried out on image dataset of soybean crop, obtained from an unmanned aerial vehicle (UAV), which is publicly available. The results from the experiment showed that the highest accuracy (above 96%) was obtained from the combination of color and LBP features.

Fouad Yacef, Nassim Rizoug, Laid Degaa

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study we introduce an optimization algorithm to minimize the consumed energy in quadrotor mission under windy conditions. The mechanical energy consumed by rotors of the flying vehicle is formulated with an efficiency function. Then, we formulate the energy minimization problem as an optimal control problem. The last problem is solved in order to calculate minimum energy for quadrotor simple mission under windy conditions. In simulation experiment, we compare the proposed method with an adaptive control approach.