Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.

The motivation of this project is to develop a high precision autonomous navigation system for precision agriculture robots. Therefore, in this paper an outdoor trajectory tracking system for a four wheel skid-steering vehicle is presented. The systems integrates an outdoor geolocation system with a tracking trajectory control system. The vehicle's outdoor geolocation system was developed by fusing GPS (Global Positioning System), magetometer and wheel encoders position information. The sensor fusion algorithm was based on the EKF (Exended Kalman Filter) and the vehicle's kinematic model. Afterwards, from top to bottom a tracking trajectory control system was designed and implemented as follows; first, a linear transfer function identification was carried out in order to model the response of the vehicle's angular direction, then, a PID controller with a feedforward compensator for vehicle's angular direction was designed. Finally, a trajectory generator as well as the trajectory tracking system were developed. The systems was implemented in a Raspberry PI-3 with an excellent processing performance. The results showed a good performance of the control tracking system, with trajectory errors of less than one meter respect to the reference trajectory, which are very promising results for tasks related to precision agriculture.

A hold-up time extension circuit (HTEC) is used to charge and discharge an auxiliary capacitor. This capacitor stores the energy required to extend the operation time of critical loads experiencing short duration failures (SDF) at the DC bus to which they are connected. This paper presents complete modeling and a control-wise approach to a parallel HTEC based on the bidirectional buck-boost converter, which operates in Boundary Conduction Mode (BCM) with a variable switching frequency. The circuit permanently regulates the voltage of the auxiliary capacitor as well as the voltage of the DC bus during SDF, which is uncommon in industrial versions of HTEC. Enforcing the operation in BCM allows a reduction in the size of the inductor in the converter without requiring additional control circuitry. The entire behavior of the proposed HTEC, in all its operation modes, was analyzed theoretically and validated using simulation and experimental results, showing the potential of the circuit to be used in real applications.