The development of the Internet of Things is essential in agriculture to meet the challenges of the agro-ecological transition. In fact, by installing communicating sensors directly in the fields, the health of ecosystems can be accurately monitored (e.g. soil conditions, crop growth, loss of biodiversity), the processes optimally controlled (e.g. irrigation systems), and the crop production better managed (e.g. optimal decision making). However, forwarding the measurements from a massive number of IoT-based sensors distributed in the fields to the internet can be a challenging task, all the more in case of energy, cost, latency, data rate or connectivity constraints. The conventional technologies, as the connection to a cellular network, a gateway or a nano-satellite at low earth orbit, may in fact not met these constraints. This paper investigates a new paradigm based on a data collector embedded on an Unmanned Aerial Vehicle (UAV). This approach has numerous advantages (e.g. no need of infrastructures, no subscription fees, operate in white areas, reduce the transmitter power of the communicating sensors). However, as the operating time of an UAV is limited, the length of the flight trajectories to visit all the sensors and collect the data must be minimized. To address this issue, the communication ranges of the sensors are first modeled as hemispheres. The Close Enough Traveling Salesman Problem (CE-TSP) is then investigated at different flying heights. To solve this problem, an algorithm based on three successive parts, a graph reduction, a partheno-genetic algorithm and heuristic rules, is developed. This algorithm is tested on data sets involving a massive number of communicating sensors with various communication ranges, as well as on a real agricultural case study. The results highlight the performances of the method proposed and open the way to future perspectives for data collection of IoT-based sensors by means of UAVs.