We present an automatic agave detection method for counting plants based on aerial data from a UAV (Unmanned Aerial Vehicle). Our objective is to autonomously count the number of agave plants in an area to aid management of the yield. An orthomosaic is obtained from agave plantations, which is then used to create a database. This database is in turn used to train a Convolutional Neural Network (CNN). The proposed method is based on computer image processing, and the CNN increases the detection performance of the approach. The main contribution of the present paper is to propose a method for agave plant detection with a high level of precision. In order to test the proposed method in a real agave plantation, we develop a UAV platform, which is equipped with several sensors to reach accurate counting. Therefore, our prototype can safely track a desired path to detect and count agave plants. For comparison purposes, we perform the same application using a simpler algorithm. The result shows that our proposed algorithm has better performance reaching an $F_1$ score of 0.96 as opposed to 0.57 for the Haar algorithm. The obtained experimental results suggest that the proposed algorithm is robust and has considerable potential to help farmers manage agave agroecosystems.

In recent years, solar energy has been used as an energy source for many different applications. Currently in the area of Unmanned Aerial Vehicles (UAVs), there are research studies that incorporate this renewable energy technology to increase the vehicle’s autonomy. This technique also needs particular construction techniques and electronic boards, designed to reduce weight and increase the efficiency of all solar systems on board the UAV. As is well known, the amount of generated solar energy could be increased throughout a day a sun tracking technique is added. The present paper proves that the roll angle of a fixed wing UAV can be used to track the sun to increase the energy generated by the solar panels placed on the wing. In that case, the plane’s attitude must be compensated with the yaw angle control to be able to perform a photogrammetric mission. This will be achieved using a control strategy based on the super-twisting technique that ensures convergence in finite time even in the presence of bounded perturbations. The design of the control laws as well as the numerical simulation and real flight results are shown to validate the use of the sun tracking system.