Abstract
The most current forecasts point to a decrease in the amount of potable water available. This increase in water scarcity is a problem with which sustainable agricultural production is facing. This has led to an increasing search for technical solutions in order to improve the efficiency of irrigation systems. In this context, this work describes the architecture of an agent-based network and the cyberphysical elements which will be deployed in a strawberry fertigation production plant. The operation of this architecture relies on local information provided by LoRA based wireless sensor network that is described in this paper. Using the information provided by the array of measurement nodes, cross-referenced with local meteorological data, grower experience and the actual crop vegetative state, it will be possible to better define the amount of required irrigation solution and then to optimise the water usage. © Springer Nature Switzerland AG 2019.

Abstract
Tawny Port wine is a category of the famous Portuguese fortified wine commercialized worldwide and produced in the Douro Demarcated Region. Tawny Port wine oxidative aging is a multifactorial process critical for reaching the wanted quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect both 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. This study presents the design, development and implementation of a remote distributed system to monitor parameters that are known to be critical for Tawny Port wine aging process. Results indicate that the distributed monitoring system was capable to detect differences between oak wood barrels and between the different storage conditions. Indeed, oxygen and redox potential were the wine's parameters where the differences found between different barrels were greater under the same storage conditions. Considering that Tawny Port wine aging process is oxidative, a variation in the wine's aging process between different wood barrels is to be expected. Actually, significant differences were detected in the oxygen consumption rate amongst the different barrels. Differences in the phenolic composition was also observed in the aged wine (controlled temperature and room temperature).

Abstract
This work aims to develop an embedded system and mobile application within Internet of Things (IoT) context, which allow the evaluation of control techniques widely used in the industry, for both remote configuration and monitoring of environment inside storage silos in laboratory scale. The developed system contributes to the validation of the recent released KNoT meta platform and extends its application to agricultural area. Preliminary results for on-off and PID temperature control suggest its feasibility for remote transmission of setpoint data to local controllers and data acquisition for monitoring by a mobile application. © Springer Nature Switzerland AG 2019.

Abstract
This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.

Abstract
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 (R) and Raspberry Pi (RN) 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
Vine culture is influenced by many factors, such as the weather, soil or topography, which are triggers to phytosanitary issues. Among them are some diseases, that are responsible for major economic losses that can, however, be managed with timely interventions in the field, viable of leading to effective results by preventing damage propagation. While not all symptoms might present a visible evidence, hyperspectral sensors can tackle this aspect with their ability for measuring hundreds of continuously sparse bands that range beyond the eye-perceptible spectrum. Having such research line in mind in this work, a hyperspectral sensor was applied to analyse the spectral status of vine leaves samples, collected in three chronologically distinct campaigns, while costly and destructive laboratory methods were used to track Flavescence Dorée (FD) in the same samples, for a ground truth information. Regarding data processing, machine learning approaches were used, in which several classifiers were selected to detect FD in vine leaves hyperspectral images. The goal was to evaluate and find most suitable classifier for this task. © 2019 International Society for Photogrammetry and Remote Sensing.