Abstract
Most data streams systems that use online Multi-target regression yield vast amounts of data which is not targeted. Targeting this data is usually impossible, time consuming and expensive. Semi-supervised algorithms have been proposed to use this untargeted data (input information only) for model improvement. However, most algorithms are adapted to work on batch mode for classification and require huge computational and memory resources. Therefore, this paper proposes an semi-supervised algorithm for online processing systems based on AMRules algorithm that handle both targeted and untargeted data and improves the regression model. The proposed method was evaluated through a comparison between a scenario where the untargeted examples are not used on the training and a scenario where some untargeted examples are used. Evaluation results indicate that the use of the untargeted examples improved the target predictions by improving the model.

Abstract

Abstract

Abstract
Crop monitoring and harvesting by ground robots in steep slope vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the Global Positioning System (GPS). In this paper the use of agricultural wireless sensors as artificial landmarks for robot localization is explored. The Received Signal Strength Indication (RSSI), of Bluetooth (BT) based sensors/technology, has been characterized for distance estimation. Based on this characterization, a mapping procedure based on Histogram Mapping concept was evaluated. The results allow us to conclude that agricultural wireless sensors can be used to support the robot localization procedures in critical moments (GPS blockage) and to create redundant localization information.

Abstract

Abstract