Abstract
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
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
The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% <= CV <= 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (-0.50 <= r <= 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Abstract
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
Developed focusing agriculture sustainability, mySense is a comprehensive close-range sensor-based data management environment to improve precision farming practices. It integrates discussion platforms for quick problem solving through experts support and a computational intelligence layer for multipurpose application (e.g. vine variety discrimination, plant disease detection and identification). Attending the need for keeping track of agricultural crops not only based on close-range sensing but also at a macro perspective, mySense was complemented with proper functionalities to unlock macro-monitoring features, through the implementation of a Web-based Geographical Information System (WebGIS) planned as a sidekick application that provides agriculture professionals with visual decision support tools over remote sensed data. This paper presents and discusses its specification and implementation.

Abstract
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.