Exploiting an Easy to Use Machine Learning Platform to Improve the Harvesting Efficiency in Orchards: Preliminary Results

Since fruit plays an important role in the human diet, pomiculture has a significant economic impact on agricultural countries worldwide. However, the lack of manual labor makes it necessary to explore alternative solutions for the harvesting process. Emerging technologies that are modernizing agriculture, including robotics, machine learning, and drones, provide a promising direction to address these types of challenges. More specifically, the use of automated harvesting robotic systems could mitigate labor shortages by operating more hours than humans and reducing the physical demands of traditional manual harvesting. The widely available, cost-effective electronics and sensors, along with the easily developed machine learning models, make it feasible to construct robotic systems for autonomous detection and picking of fruits. On the other hand, the deployment of robotic systems in orchards remains a challenge due to the dynamic and unpredictable nature of these environments, leaving room for further research. This paper aims to develop a machine learning architecture to identify oranges and orange trees in orchard environments, using both ground and UAV imagery. The proposed model is an integral part of a ground-based autonomous robotic harvesting system. This paper describes the methodology followed, discusses the preliminary results and proposes future work to contribute to the field of automated agricultural systems. Despite the medium-size dataset and the contribution of a user-friendly platform for developing models for edge devices, the results of the models are very encouraging, and through the evaluation the most suitable algorithms were selected for detecting trees upon entering an orchard and oranges at close range.