Abstract
Labour shortage is a reality in agriculture. Farmers are asking for solutions to automate agronomic tasks, such as monitoring, pruning, spraying, and harvesting. The automation of these tasks requires, most of the time, the use of robotic arms to mimic human arms capabilities. The current robotic arm based solutions available, both in the market and in the scientific sphere, have several limitations, such as, low-speed manipulation, the path planning algorithms are not aware of the requirements of the agricultural tasks (robotic motion and manipulation synchronisation), and require active perception tuning to the end-target point. This work benchmarks algorithms from open manipulation planning library (OMPL) considering a cost-effective six-degree freedom manipulator in a simulated vineyard. The OMPL planners shown a very low performance under demanding pruning tasks. The best and most promising results are performed and obtained by BiTRRT. However, further work is needed to increase its performance and reduce planning time. This benchmark work helps the reader to understand the limitations of each algorithm and when to use them. © 2019, Springer Nature Switzerland AG.

Abstract
Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC-FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.

Abstract
Placing ground robots to work in steep slope vineyards is a complex challenge. The Global Positioning System (GPS) signal is not always available and accurate. A reliable localization approach to detect natural features for this environment is required. This paper presents an improved version of a visual detector for Vineyards Trunks and Masts (ViTruDe) and, a robot able to cope pruning actions in steep slope vineyards (AgRob V16). In addition, it presents an augmented data-set for other localization and mapping algorithm benchmarks. ViTruDe accuracy is higher than 95% under our experiments. Under a simulated runtime test, the accuracy lies between 27% - 96% depending on ViTrude parametrization. This approach can feed a localization system to solve a GPS signal absence. The ViTruDe detector also considers economic constraints and allows to develop cost-effective robots. The augmented training and datasets are publicly available for future research work.

Abstract
Metabolomics is paramount for precision agriculture. Knowing the metabolic state of the vine and its implication for grape quality is of outermost importance for viticulture and wine industry. The MetBots system is a metabolomics precision agriculture platform, for automated monitoring of vineyards, providing geo-referenced metabolic images that are correlated and interpreted by an artificial intelligence self-learning system for aiding precise viticultural practices. Results can further be used to analyze the plant metabolic response by genome-scale models. In this research, we introduce the system main components: (i) robotic platform; (ii) autonomous navigation; (iii) sampling arm manipulation; (iv) spectroscopy systems; and (v) non-invasive, real-time metabolic hyper-spectral imaging monitoring of vineyards. The full potential of the Metbots system is revealed when metabolic data and images are analyzed by big data AI and systems biology vine plant models, establishing a new age of molecular biology precision agriculture. © Springer Nature Switzerland AG 2019.

Abstract
In order to determine the physiological state of a plant it is necessary to monitor it throughout the developmental period. One of the main parameters to monitor is the Leaf Area Index (LAI). The objective of this work was the development of a non-destructive methodology for the LAI estimation in wine growing. This method is based on stereo images that allow to obtain a bard 3D representation, in order to facilitate the segmentation process, since to perform this process only based on color component becomes practically impossible due to the high complexity of the application environment. In addition, the Normalized Difference Vegetation Index will be used to distinguish the regions of the trunks and leaves. As an low-cost and non-evasive method, it becomes a promising solution for LAI estimation in order to monitor the productivity changes and the impacts of climatic conditions in the vines growth. © Springer Nature Switzerland AG 2019.

Abstract
Emergent and established paradigms, such as the Internet of Things (IoT), cloud and fog/edge computing, together with increasingly cheaper computing technologies – with very low power requirements, available to exchange data with increased efficiency – and intelligent systems, have evolved to a level where it is virtually possible to create and deploy monitoring solutions, even in Precision Agriculture (PA) practices. In this work, LoRa®(Long Range) technology and LoRaWAN™protocol, are tested in a Precision Viticulture (PV) scenario, using low-power data acquisition devices deployed in a vineyard in the UTAD University Campus, distanced 400 m away from the nearest gateway. The main goal of this work is to evaluate sensor data integration in the mySense environment, a framework aimed to systematize data acquisition procedures to address common PA/PV issues, using LoRa®technology. mySense builds over a 4-layer technological structure: sensor and sensor nodes, crop field and sensor networks, cloud services and front-end applications. It makes available a set of free tools based on the Do-It-Yourself (DIY) concept and enables the use of low-cost platforms to quickly prototype a complete PA/PV monitoring application. © Springer Nature Switzerland AG 2019.