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• Analyse and review the monitoring chain of an agricultural process. • Systematize the use of data acquisition and data integration platforms. • Propose free DIY tools to quickly create monitoring solutions for PA/PV. • Intensify the use of technology in PA/PV practices. • mySense as a solution to address common PA/PV issues related to data management. Over the last few years, an extensive set of technologies have been systematically included in precision agriculture (PA) and also in precision viticulture (PV) practices, as tools that allow efficient monitoring of nearly any parameter to achieve sustainable crop management practices and to increase both crop yield and quality. However, many technologies and standards are not yet included on those practices. Therefore, potential benefits that may result from putting together agronomic knowledge with electronics and computer technologies are still not fully accomplished. Both emergent and established paradigms, such as the Internet of Everything (IoE), Internet of Things (IoT), cloud and fog computing, together with increasingly cheaper computing technologies – with very low power requirements and a diversity of wireless technologies, available to exchange data with increased efficiency – and intelligent systems, have evolved to a level where it is virtually possible to expeditiously create and deploy any required monitoring solution. Pushed by all of these technological trends and recent developments, data integration has emerged as the layer between crops and knowledge needed to efficiently manage it. In this paper, the mySense environment is presented, aimed to systematize data acquisition procedures to address common PA/PV issues. mySense builds over a 4-layer technological structure: sensor and sensor nodes, crop field and sensor networks, cloud services and support to front-end applications. It makes available a set of free tools based on the Do-It-Yourself (DIY) concept and enables the use of Arduino® and Raspberry Pi (RPi) low-cost platforms to quickly prototype a complete monitoring application. Field experiments provide compelling evidences that mySense environment represents an important step forward towards Smart Farming, by enabling the use of low-cost, fast deployment, integrated and transparent technologies to increase PA/PV monitoring applications adoption. [ABSTRACT FROM AUTHOR]

Aging of Tawny Port wine is a multifactorial process critical for attaining the desired quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect the time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. For this study, a distributed monitoring system was installed in sixteen oak barrels, placed in two adjacent wineries – one of them with controlled temperature – in the Douro Demarcated Region, Portugal. The monitoring process was performed using a RS-485 industrial network, which interconnects sensors that continuously measure wine temperature, pH, redox potential and wine’s dissolved oxygen, as well as other sensors that measure parameters related to the barrels’ environmental context, such as room temperature and relative humidity. This work presents the design, development and implementation of a remote distributed system to monitor such parameters, aiming to determine the existence of behaviour models for Port Tawny wine during aging in long-used oak barrels, depending on their storage history and to understand the evolution of wine pH, dissolved oxygen and redox potential in real winery conditions as well as their dependence on the wine’s storage temperature. This approach is based on easy-to-use embedded systems, with the aim of giving a relevant contribution to other projects in the area of precision enology. [ABSTRACT FROM AUTHOR]

Abstract: Ever since the first studies about biomedical implantable devices, the problem of how to energize them has stood out as both important and notoriously difficult to solve. In order to extend the lifetime of implants, it is imperative to develop power generators that are autonomous, safe and maintenance-free. Energy harvesting is a natural way of meeting these requirements. First, the energy source is theoretically everlasting, a fact that helps to guarantee the autonomy. Second, the energy is obtained from the environment of the application itself, contributing to its safety. Finally, a properly designed energy harvesting system is very unlikely to ever require maintenance. This paper follows this line and describes an electromagnetic power transducer that harvests electrical energy from the human gait and stores it. An efficient power management module uses the stored energy to energize the telemetric system of a smart hip prosthesis implant, enabling the early detection of loosening, the target application of this work. The system is able to extract a total 1912.5μJ of usable energy under normal walking conditions. [Copyright &y& Elsevier]

Abstract: Biomedical implantable devices in real-time telemetry applications offer many advantages to study and monitor physiologic parameters in human and animal bodies. As in power-constrained implantable devices, batteries and wires are two key issues that usually compromise their application. Inductive coupling is a well-established technology because it replaces simultaneously the wired connection and avoids the use of batteries. In stand-alone applications that requires total freedom of movements, this technology may present some constrains. On the other hand, very-low power electronics, advanced energy harvesting techniques and smaller implantable rechargeable batteries makes now possible the conception of fully autonomous implantable devices. This paper describes an activation circuit used to completely turn-off battery-powered implantable devices. In addition, it allows the reception of embedded commands, suitable for calibration and sensor selection purposes. Results from an experimental device to be used in a smart hip prosthesis telemetry system for loosening detection shows the usefulness of the proposed activation concept. [Copyright &y& Elsevier]

Abstract: The deployment of large mesh-type wireless networks is a challenge due to the multitude of arising issues. Perpetual operation of a network node is undoubtedly one of the major goals of any energy-aware protocol or power-efficient hardware platform. Energy harvesting has emerged as the natural way to keep small stationary hardware platforms running, even when operating continuously as network routing devices. This paper analyses solar radiation, wind and water flow as feasible energy sources that can be explored to meet the energy needs of a wireless sensor network router within the context of precision agriculture, and presents a multi-powered platform solution for wireless devices. Experimental results prove that our prototype, the MPWiNodeX, can manage simultaneously the three energy sources for charging a NiMH battery pack, resulting in an almost perpetual operation of the evaluated ZigBee network router. In addition to this, the energy scavenging techniques double up as sensors, yielding data on the amount of solar radiation, water flow and wind speed, a capability that avoids the use of specific sensors. [Copyright &y& Elsevier]

Abstract: This paper is part of a long-term effort to introduce precision viticulture in the region of Demarcated Region of Douro. It presents the architecture, hardware and software of a platform designed for that purpose, called MPWiNodeZ. A major feature of this platform is its power-management subsystem, able to recharge batteries with energy harvested from the surrounding environment from up to three sources. It allows the system to sustain operation as a general-purpose wireless acquisition device for remote sensing in large coverage areas, where the power to run the devices is always a concern. The MPWiNodeZ, as a ZigBee™ network element, provides a mesh-type array of acquisition devices ready for deployment in vineyards. In addition to describing the overall architecture, hardware and software of the monitoring system, the paper also reports on the performance of the module in the field, emphasising the energy issues, crucial to obtain self-sustained operation. The testing was done in two stages: the first in the laboratory, to validate the power management and networking solutions under particularly severe conditions, the second stage in a vineyard. The measurements about the behaviour of the system confirm that the hardware and software solutions proposed do indeed lead to good performance. The platform is currently being used as a simple and compact yet powerful building block for generic remote sensing applications, with characteristics that are well suited to precision viticulture in the DRD region. It is planned to be used as a network of wireless sensors on the canopy of vines, to assist in the development of grapevine powdery mildew prediction models. [Copyright &y& Elsevier]

Abstract: In cattle breeding industry, where artificial insemination techniques are employed, the successful detection of estrus onset leads to considerable cost-saving in herd management. One of the most reliable approaches is based on the determination of progesterone concentration in milk. However, these methods rely on the biosensor concept where a biological substrate is used in a chemical-binding reaction to directly or indirectly produce some effect (electrical or light) that is used at the transducer level. These methods present several drawbacks concerning real-time measurements due to the complexity of the reactions involved and reagent/waste handling. Another approach is to combine measurements of temperature and electrical resistance of reproductive tissues to predict estrus. Using a low-power microsystem with wireless capabilities it is possible to take these measurements in situ and more frequently. The proposed microsystem comprises a second-order delta–sigma modulator for analog-to-digital conversion and a class-E radio-frequency (RF) transmitter operating in the ISM-band of 433 MHz to transfer acquired data to a collar. Electrical resistance is measured by using a modified Wenner array and temperature by the on-chip temperature sensor. System (including battery and antenna) package is made of a tissue-compatible material to allow implantation in the cow’s vulvar muscle. Since estrus prediction is based on relative changes of the two correlated parameters, calibration is not necessary. Some preliminary results regarding the measuring concept are presented. [Copyright &y& Elsevier]

This paper describes a wireless RF CMOS interface for soil moisture measurements. The interface basically comprises a Delta-Sigma (ΔΣ) modulator for acquiring an external sensor signal, and a RF section where data is transmitted to a local processing unit. The ΔΣ modulator is a single-bit, second-order modulator and it is implemented using switched-capacitors techniques in a fully-differential topology. With a sampling frequency of 423.75 kHz and an oversampling ratio (OSR) of 256, the modulator achieves a dynamic range of 98.7 dB (16.1 bit). The output of the modulator is applied to a counter, as a first-order decimation filter, and the result is stored. Prior to transmission, data is encoded as a pulse width modulated signal and assembled in a frame containing preamble and checksum control fields. This frame is then transmitted through a power amplifier operating at 433.92 MHz in class-E mode. To evaluate the ΔΣ modulator performance, the bitstream was acquired and transferred to a personal computer to perform digital filtering and decimation using MATLAB. The soil moisture sensor is based on dual-probe heat-pulse (DPHP) method and is implemented by using an integrated temperature sensor and a heater. After applying a heat-pulse for a fixed period of time, the temperature rise, that is a function of soil moisture, generates a differential voltage that is amplified and applied to the mixed-signal interface input. The described interface can also be used with other kinds of environmental sensors in a wireless sensors network. The CMOS mixed-signal interface has been implemented in a single-chip using a standard CMOS 0.7 μm process (AMI C07M-A, n-well, 2 metals and 1 poly). [Copyright &y& Elsevier]

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties. [ABSTRACT FROM AUTHOR]

The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute force-based K-fold cross-validation or the naivety of hold-out are required, with the following main goals in mind: (1) carrying out one-shot, close-to-optimal data arrangements, accelerating conventional training optimization; and (2) aiming at maximizing the capacity of inference models to its fullest extent while relieving computational burden. To that end, in this paper, two image-based feature-aware dataset splitting approaches are proposed, hypothesizing a contribution towards attaining classification models that are closer to their full inference potential. Both rely on strategic image harvesting: while one of them hinges on weighted random selection out of a feature-based clusters set, the other involves a balanced picking process from a sorted list that stores data features' distances to the centroid of a whole feature space. Comparative tests on datasets related to grapevine leaves phenotyping and bridge defects showcase promising results, highlighting a viable alternative to K-fold cross-validation and hold-out methods. [ABSTRACT FROM AUTHOR]

Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type' datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors' performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new low power electrical conductance converter is presented. [•] Converter topology features and a CMOS implementation are described. [•] The implemented prototype consumption is under 1.175μW when powered at 1.135V. [ABSTRACT FROM AUTHOR]

Abstract: Wireless sensor networks have found multiple applications in precision viticulture. Despite the steady progress in sensing devices and wireless technologies, some of the crucial items needed to improve the usability and scalability of the networks, such as gateway infrastructures and in-field processing, have been comparatively neglected. This paper describes the hardware, communication capabilities and software architecture of an intelligent autonomous gateway, designed to provide the necessary middleware between locally deployed sensor networks and a remote location within the whole-farm concept. This solar-powered infrastructure, denoted by iPAGAT (Intelligent Precision Agriculture Gateway), runs an aggregation engine that fills a local database with environmental data gathered by a locally deployed ZigBee wireless sensor network. Aggregated data are then retrieved by external queries over the built-in data integration system. In addition, embedded communication capabilities, including Bluetooth, IEEE 802.11 and GPRS, allow local and remote users to access both gateway and remote data, as well as the Internet, and run site-specific management tools using authenticated smartphones. Field experiments provide convincing evidence that iPAGAT represents an important step forward in the development of distributed service-oriented information systems for precision viticulture applications. [Copyright &y& Elsevier]

Abstract: This paper describes a Viticulture Service-Oriented Framework (VSOF) which turns around context elements or tags that are placed in the field and which can be decoded by mobile devices such as mobile phones or PDAs. The tags are used to automatically associate a field location to the relevant database tables or records and also to access contextual information or services. By pointing a mobile device to a tag, the viticulturalist may download data such as climatic data or upload information such as disease and pest incidence in a simple way, without having to provide coordinates or any other references, and without having to return to a central office. This work is part of an effort to implement a large-scale distributed cooperative network in the Douro Demarcated Region in Northeast Portugal, a region in which the effort makes particular sense due to the extremely variable topography and mesoclimates. The possibility of exchanging contextualized information and accessing contextualized services in the field, using well-known devices such as cell phones, may contribute to increase the rate of adoption of information technology in viticulture, and contribute to more efficient and closer-to-the-crops practices. [Copyright &y& Elsevier]

Abstract: Vehicles rely on the efficiency of dampers to dissipate energy from the motion of vehicle body and wheels, maintaining the vehicle more stable, and improving the contact between tires and the road surface. To achieve an effective monitoring of dampers (or shock absorbers), two different methodologies, capable of assessing, under vehicle normal operation, the condition of the automotive dampers are presented. The proposed methodologies are based in acceleration, temperature and pressure sensing to determine the shock absorber condition, and are therefore suitable for future implementation in low cost fabrication technologies. The results shown that it is possible to have an effective monitoring device, installed in the damper body, capable of continuously determining shock absorber status, and therefore enabling real time diagnosis. Such a diagnosis system can reduce the number of vehicles riding with defective suspension systems and increase the overall vehicle safety. [Copyright &y& Elsevier]

Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution—for the automotive industry—to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant. [ABSTRACT FROM AUTHOR]

• Current micro-scale biomagnetic devices require very high electric current excitations. • A novel biomagnetic methodology requiring up to 50-fold lower currents is proposed. • Osteogenic stimuli (0.1–7 mT) is achieved by low current excitations (0.06–4.3 mA). • Device designs with heights lower than 1 mm are able to deliver effective stimuli. • Stimuli heterogeneity can be defined by core diameters and stimulators positioning. Recent studies highlight the ability of inductive architectures to deliver therapeutic magnetic stimuli to target tissues and to be embedded into small-scale intracorporeal medical devices. However, to date, current micro-scale biomagnetic devices require very high electric current excitations (usually exceeding 1 A) to ensure the delivery of efficient magnetic flux densities. This is a critical problem as advanced implantable devices demand self-powering, stand-alone and long-term operation. This work provides, for the first time, a novel small-scale magnetic stimulation system that requires up to 50-fold lower electric current excitations than required by relevant biomagnetic technology recently proposed. Computational models were developed to analyse the magnetic stimuli distributions and densities delivered to cellular tissues during in vitro experiments, such that the feasibility of this novel stimulator can be firstly evaluated on cell culture tests. The results demonstrate that this new stimulative technology is able to deliver osteogenic stimuli (0.1–7 mT range) by current excitations in the 0.06–4.3 mA range. Moreover, it allows coil designs with heights lower than 1 mm without significant loss of magnetic stimuli capability. Finally, suitable core diameters and stimulator-stimulator distances allow to define heterogeneity or quasi -homogeneity stimuli distributions. These results support the design of high-sophisticated biomagnetic devices for a wide range of therapeutic applications. [ABSTRACT FROM AUTHOR]

To differentiate between canopy and vegetation cover is particularly challenging. Nonetheless, it is pivotal in obtaining the exact crops' vegetation when using remote-sensing data. In this article, a method to automatically estimate and extract vineyards' canopy is proposed. It combines vegetation indices and digital elevation models - derived from high-resolution images, acquired using unmanned aerial vehicles - to differentiate between vines' canopy and inter-row vegetation cover. This enables the extraction of relevant information from a specific vineyard plot. The proposed method was applied to data acquired from some vineyards located in Portugal's north-eastern region, and the resulting parameters were validated. It proved to be an effective method when applied with consumer-grade sensors, carried by unmanned aerial vehicles. Moreover, it also proved to be a fast and efficient way to extract vineyard information, enabling vineyard plots mapping for precision viticulture management tasks. [ABSTRACT FROM AUTHOR]

Traditional imagery--provided, for example, by RGB and/or NIR sensors--has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry--wherein the combination of UAV and hyperspectral sensors plays a center role--is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing--by isolating the common user from the processes' mathematical complexity--several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions. [ABSTRACT FROM AUTHOR]

Animal experiments have gained importance in human diseases studies, namely neurological diseases and its biomechanical and physiological aspects. As a model of human disease, the rat offers many advantages over other organisms. For the biomechanical aspects of locomotion these studies are based on the analysis of animals’ kinetic parameters, accessed through a locomotion measurement system. However, these systems are not yet thoroughly developed, are still scarce and are also very expensive when developed for studies using small rodents. In this paper, a system capable of measuring contact forces of small rodents is presented. The platform hardware is based on a 5 × 3 matrix of ultra-sensitive force sensors that produce a set of signals acquired in a LabVIEW TM environment, used for data acquisition and processing. The post processing steps include the removal of null data, curve normalization related to the rat’s weight and expressed as percentage of passage, resulting in a gait pattern. The proposed cost-effective system has achieved excellent results regarding the locomotion profile of healthy animals. [ABSTRACT FROM AUTHOR]

The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite Systems (GNSS) and high quality and cost-effective sensors; second, to advice the target audience – mostly farmers and foresters – how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels’ size and crop’s types.Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors’ data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions. [ABSTRACT FROM PUBLISHER]

• Machine learning for vineyard classification using different UAV-based datasets. • OBIA method to perform vineyard classification. • Artificial neural networks reached the best performance in all evaluated vineyards. • Performance decreases in the cases where the crop surface model is not used. • RGB data has higher contribution than multispectral, better performance using both. Vineyard classification is an important process within viticulture-related decision-support systems. Indeed, it improves grapevine vegetation detection, enabling both the assessment of vineyard vegetative properties and the optimization of in-field management tasks. Aerial data acquired by sensors coupled to unmanned aerial vehicles (UAVs) may be used to achieve it. Flight campaigns were conducted to acquire both RGB and multispectral data from three vineyards located in Portugal and in Italy. Red, green, blue and near infrared orthorectified mosaics resulted from the photogrammetric processing of the acquired data. They were then used to calculate RGB and multispectral vegetation indices, as well as a crop surface model (CSM). Three different supervised machine learning (ML) approaches—support vector machine (SVM), random forest (RF) and artificial neural network (ANN)—were trained to classify elements present within each vineyard into one of four classes: grapevine, shadow, soil and other vegetation. The trained models were then used to classify vineyards objects, generated from an object-based image analysis (OBIA) approach, into the four classes. Classification outcomes were compared with an automatic point-cloud classification approach and threshold-based approaches. Results shown that ANN provided a better overall classification performance, regardless of the type of features used. Features based on RGB data showed better performance than the ones based only on multispectral data. However, a higher performance was achieved when using features from both sensors. The methods presented in this study that resort to data acquired from different sensors are suitable to be used in the vineyard classification process. Furthermore, they also may be applied in other land use classification scenarios. [ABSTRACT FROM AUTHOR]

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the "Mar Menor", the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier. [ABSTRACT FROM AUTHOR]

Abstract: Methane (CH4) and nitrous oxide (N2O) emissions from flooded rice fields have been rarely measured in Europe. A field study was carried out in an intermittent flooded rice field at central Portugal to investigate if global warming under Mediterranean conditions, elevated soil temperature (+2 °C) and atmospheric [CO2] (550 ppm), could lead to significant effects in CH4 and N2O emissions. The experimental design consisted of three treatments arranged in a randomized complete block design with three replicates. To assess the effects of ambient temperature and actual atmospheric [CO2] (375 ppm), plots were laid under open-field rice conditions. Using open-top chambers, two other treatments were established: one to assess the effect of elevated temperature and actual atmospheric [CO2] and a third treatment to evaluate the combined effect of elevated temperature and atmospheric [CO2]. Measurements of CH4 and N2O fluxes were made throughout two consecutive growing seasons in the field using the closed chamber technique. Elevation of temperature with or without elevated atmospheric [CO2] increased CH4 emissions by 50%, but this increase was not significant compared to the open-field condition. As for N2O, elevated temperature alone or combined with elevated atmospheric [CO2] had no significant effect on emissions relative to the open-field treatment. The estimated seasonal CH4 EF for the Portuguese flooded rice fields was 10.0 g CH4 m−2, while the EF for N2O emissions was 1.4% of N input. These results suggested that default seasonal CH4 and N2O EFs currently used by the Portuguese inventory were not appropriated. [Copyright &y& Elsevier]

Abstract: Energy harvesting solutions such as instrumented orthopaedic implants are under development to power a wide variety of electronic systems including biomedical implants. Three micro-power generators have already been developed as part of a smart hip prosthesis structure. This paper outlines a power management architecture for efficient harvesting of energy to supply power to modules other than those powered by current instrumented implants. Considering that it is impossible to predict the amount of energy harvested by each particular person, the proposed system also comprises an activation circuit and its ultracapacitor energy reservoir as a fourth type of energy to be used when a continuous energy source is needed. The hip prosthesis prototype has now the capability to energize more power demanding loads, intermittently or continuously, such as radio-frequency modules. The proposed architecture enables operation of a Bluetooth low energy (V4.0) embedded device (BLE112 from Bluegiga), part of a wireless body sensor network, up to 50s, and a MSP430/eZ430-RF2500 (Texas Instruments), which uses the SimpliciTI communication protocol, up to 110s, solely using the energy produced by one of the generators. [Copyright &y& Elsevier]

Instrumented hip implants were proposed as a method to monitor and predict the biomechanical and thermal environment surrounding such implants. Nowadays, they are being developed as active implants with the ability to prevent failures by loosening. The generation of electric energy to power active mechanisms of instrumented hip implants remains a question. Instrumented implants cannot be implemented without effective electric power systems. This paper surveys the power supply systems of seventeen implant architectures already implanted in-vivo, namely from instrumented hip joint replacements and instrumented fracture stabilizers. Only inductive power links and batteries were used in-vivo to power the implants. The energy harvesting systems, which were already designed to power instrumented hip implants, were also analyzed focusing their potential to overcome the disadvantages of both inductive-based and battery-based power supply systems. From comparative and critical analyses of the methods to power instrumented implants, one can conclude that: inductive powering and batteries constrain the full operation of instrumented implants; motion-driven electromagnetic energy harvesting is a promising method to power instrumented passive and active hip implants. [ABSTRACT FROM AUTHOR]

Highlights: [•] IEEE 1451 framework for managing IEEE 802.15.4/ZigBee sensor networks in PA/PV applications. [•] IEEE 1451 NCAP implementation over an in-field gateway (iPAGAT) that collects data from heterogeneous data sources. [•] Smart and unmanned wireless data acquisition device that operates as a IEEE 1451 WTIM without the need of polling. [•] Normalized procedures for distributed data acquisition procedures over mesh-type ZigBee sensor networks. [•] Combination iPAGAT/NCAP allows the creation of smart, easy-to-configure systems. [Copyright &y& Elsevier]

Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed. [ABSTRACT FROM AUTHOR]

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations. [ABSTRACT FROM AUTHOR]