Abstract
In the early 1980's, the European chestnut tree (Castanea sativa, Mill.) assumed an important role in the Portuguese economy. Currently, the Trás-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region (€50M-€60M). The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Trás-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease (Phytophthora cinnamomi) and the chestnut blight (Cryphonectria parasitica), along with other threats, e.g. chestnut gall wasp (Dryocosmus kuriphilus) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation. Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
Authenticity, traceability and certification are key to assure both quality and confidence to wine consumers and an added commercial value to farmers and winemakers. Grapevine variety stands out as one of the most relevant factors to be considered in wine identification within the whole wine sector value chain. Ampelography is the science responsible for grapevine varieties identification based on (i) in-situ visual inspection of grapevine mature leaves and (ii) on the ampelographer experience. Laboratorial analysis is a costly and time-consuming alternative. Both the lack of experienced professionals and context-induced error can severely hinder official regulatory authorities’ role and therefore bring about a significant impact in the value chain. The purpose of this paper is to assess deep learning potential to classify grapevine varieties through the ampelometric analysis of leaves. Three convolutional neural networks architectures performance are evaluated using a dataset composed of six different grapevine varieties leaves. This preliminary approach identified Xception architecture as very promising to classify grapevine varieties and therefore support a future autonomous tool that assists the wine sector stakeholders, particularly the official regulatory authorities. © Springer Nature Switzerland AG 2019.

Abstract
Viticulture has a major impact in the European economy and over the years the intensive grapevine production led to the proliferation of many varieties. Traditionally these varieties are manually catalogued in the field, which is a costly and slow process and being, in many cases, very challenging to classify even for an experienced ampelographer. This article presents a cost-effective and automatic method for grapevine varieties classification based on the analysis of the leaf’s images, taken with an RGB sensor. The proposed method is divided into three steps: (1) color and shape features extraction; (2) training and; (3) classification using Linear Discriminant Analysis. This approach was applied in 240 leaf images of three different grapevine varieties acquired from the Douro Valley region in Portugal and it was able to correctly classify 87% of the grapevine leaves. The proposed method showed very promising classification capabilities considering the challenges presented by the leaves which had many shape irregularities and, in many cases, high color similarities for the different varieties. The obtained results compared with manual procedure suggest that it can be used as an effective alternative to the manual procedure for grapevine classification based on leaf features. Since the proposed method requires a simple and low-cost setup it can be easily integrated on a portable system with real-time processing to assist technicians in the field or other staff without any special skills and used offline for batch classification. © Springer Nature Switzerland AG 2019.

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
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
Vibrational energy harvesters for powering wearable electronics and other electrical energy demanding devices are among the most used approaches. Devices that use magnetic forces to maintain the central mass in magnetic levitation, aligned with several coils as the emf generating transducer mechanism, are becoming a suitable choice since they do not need the usual spring that typically degrades over time. Modeling such energy harvesters poses different challenges due to the difficulty of getting the nonlinear closed-form expression that would describe the resulting magnetic force of the entire system. In this paper, modeling of the magnetic forces resulting from the system magnets interaction is presented. Results give valuable data about how the best energy harvester should be designed taking into account resonance frequency related to system’s mass and dimensions.