The UPAO develops a robot to improve the planning of the blueberry harvests in Peru
A project carried out by the Antenor Orrego Private University (UPAO) of Trujillo, which seeks to im…A project carried out by the Antenor Orrego Private University (UPAO) of Trujillo, which seeks to improve blueberry production in the north of the country, has developed a self-locating robotic system with the ability to map its environment and process data through artificial intelligence, which will help improve the agricultural projections of the blueberry sector.
“This robot evaluates the blueberries in the bush: it detects them, counts them, and assesses their degree of maturity according to the color of each fruit. Producers can use that information to improve their production volume or plan harvests, for example, in the next two weeks or in a month. That's the added value it can give the productive sector,” stated Sixto Prado, electronic engineer, professor, and researcher at UPAO that is in charge of the project.
“In terms of mobility, it is 100% operational in agricultural fields. We've been improving its fruit counting ability with each of the tests we carried out because there are different variables we must take into account, such as the speed at which the robot must move, or the most appropriate distance there must be from the camera and the sensors to the bushes. The goal is to achieve a 95% or higher accuracy,” he stated.
Many of the companies working with blueberries in the north of the country have already expressed their interest in the robot. “They have visited us because they are interested in the potential it may have. They are interested in how it can help them plan their harvests. The robot doesn't harvest the fruits, it evaluates them. With its data, for example, producers are able to determine when and in which sectors of their fields they'll require more personnel to harvest the blueberries. If they don't calculate this correctly, they might need more or fewer workers, or not have enough of them on time,” Prado added.
In addition, this project works for all types of crops. With the right algorithms, one could generate the same data for any other agricultural product.
