Abstract
The increasing world population, growing need for agricultural products, and labour shortages have driven the growth of robotics in agriculture. Tasks such as fruit harvesting require extensive hours of work during harvest periods and can be physically exhausting. Autonomous robots bring more efficiency to agricultural tasks with the possibility of working continuously. This paper proposes a stackable 3 DoF SCARA manipulator for tomato harvesting. The manipulator uses a custom electronic circuit to control DC motors with an endless gear at each joint and uses a camera and a Tensor Processing Unit (TPU) for fruit detection. Cascaded PID controllers are used to control the joints with magnetic encoders for rotational feedback, and a time-of-flight sensor for prismatic movement feedback. Tomatoes are detected using an algorithm that finds regions of interest with the red colour present and sends these regions of interest to an image classifier that evaluates whether or not a tomato is present. With this, the system calculates the position of the tomato using stereo vision obtained from a monocular camera combined with the prismatic movement of the manipulator. As a result, the manipulator was able to position itself very close to the target in less than 3 seconds, where an end-effector could adjust its position for the picking.

Abstract
In this work a forest fire detection solution using small autonomous aerial vehicles is proposed. The FALCOS unmanned aerial vehicle developed for remote-monitoring purposes is described. This is a small size UAV with onboard vision processing and autonomous flight capabilities. A set of custom developed navigation sensors was developed for the vehicle. Fire detection is performed through the use of low cost digital cameras and near-infrared sensors. Test results for navigation and ignition detection in real scenario are presented.

Abstract
The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab (R), allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method's simplicity, which is in continuous development in order to optimize the results.

Abstract
<p>The WATER4EVER Project (http://water4ever.eu/) was built on the premise that agriculture is by far the largest consumer of water, with about 70% of the diverted water being used in irrigation. Agriculture is also considered as a key source of diffuse pollution with inefficient practices resulting in high water and nutrient (particularly N and P) surpluses that are transferred to water bodies through diffuse processes (runoff and leaching), promoting eutrophication, with associated biodiversity loss. WATER4EVER aims thus to develop new monitoring strategies at the plot and catchment scales to provide detailed information of water and nutrient flow, and gain new insights on the connectivity between both scales. New monitoring strategies were developed and tested in agricultural fields in Portugal, Spain, Italy and Turkey and included: (i) crop physiological indicators assessment using static sensors for defining improved deficit irrigation strategies for woody crops; (ii) crop stress and productivity maps from measurements taken with a smart sensor mounted on a tractor and equipped with LIDAR 2D, normalized difference vegetation index (NDVI) and thermal cameras, and a GNSS receiver; (iii) leaf area index maps at 30 m resolution derived from ATCOR and Landsat 8 imagery data using the NDVI and the Soil Adjusted Vegetation Index (SAVI); (iv) soil moisture maps at 100 m resolution by combining the 10 m resolution synthetic-aperture radar (SAR) images from Sentinel 1 with the 10 m resolution NDVI computed from Sentinel 2 images, averaged into 100 m cells, and then by considering the backscatter difference with the driest day, or alternatively the backscatter difference between two consecutive dates; (v) soil moisture maps at 1 km resolution created with the DISaggregation based on a Physical And Theoretical scale CHange (DISPATCH) algorithm for the downscaling of the 40 km SMOS (Soil Moisture and Ocean Salinity) soil moisture data using land surface temperature (LST) and NDVI data; (vi) conventional monitoring techniques combined with modeling tools for assessing the impact of different soil managements (conventional tillage, tillage with grass trips, grass cover) on soil infiltration, soil water content, runoff and soil erosion of hillslope vineyards; (vii) an improved deterministic model for irrigation and fertigation management at the plot scale; and (viii) a decision support system for irrigation water management at the plot scale which integrated a deterministic model for irrigation scheduling and the NDVI computed from Sentinel 2 imagery data for crop growth monitoring. Preliminary results derived from the use of the innovative monitoring and mapping strategies, besides model applications are presented. The remote sensing products described above were also applied for catchment modeling validation of streamflow, which results fall outside the scope of this communication. WATER4EVER activities were thus wide and diverse, aimed at optimizing crop management practices which will help to promote the sustainability of different Mediterranean production systems.</p><p> </p><p>WATER4EVER is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme</p>

Abstract
Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.