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.

This paper presents how the nested saturation control introduced in Teel (1992) can be modified to stabilise the model of the Planar Vertical Take-Off and Landing (PVTOL) system. A particular choice of the amplitudes of the nested saturations controller is given such that global stability of the closed loop system is obtained. The proof is given in detail. Numerical simulations illustrate the performance of the proposed control algorithm.

This paper presents a novel autonomous take-off and landing approach which uses a discrete-time finite-horizon suboptimal nonlinear control strategy to achieve a smooth landing on specific colored platform. By color-based tracking we can improve the performance of the position landing by correcting the position given by the GPS. The signal of the GPS is not always reliable since the accuracy strongly depends on the environment situation and the correction by vision servoing is then necessary. Experimental results using the proposed approach show a better performance than those where the GPS signal is only used to perform the take-off and landing tasks in a completely autonomous way in presence of wind gusts.