Smart farming based on Internet of Things (IoT) technologies enables crop farmers to collect real-time data related to irrigation and plant protection processes, aiming to increase production volume, improve product quality, and predict diseases, while optimizing resources and farming processes. IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment. The aim of this study is to investigate possible advantages of applying the Smart Farming as a Service (SFaaS) paradigm, aiming to support small-scale farmers, by taking over the technological investment burden and offering next generation farming advice through the combined utilization of heterogeneous information sources. The overall results of the pilot application demonstrate a potential reduction of up to 22% on total irrigation needs and important optimization opportunities on pesticides use efficiency. The current work offers opportunities for innovation targeting and climate change adaptation options (new agricultural technologies), and could help farmers to reduce their ecological footprint.

Among the most important challenges towards the digitisation of agriculture is the high cost of technical equipment and the lack of smart farming systems' capability to interoperate. This paper presents the gaiasense ^TM solution which follows an innovative approach in offering smart-farming services as an inexpensive service with zero technological related investment for farmers. In addition, the concept of the “Data Interoperability Zone” is introduced along with the “Information Management Adapter” aiming to facilitate data interoperability for smart-farming systems.

This study presents the experimental testing of a gas-sensing array, for the detection of two commercially available pesticides (i.e., Chloract 48 EC and Nimrod), towards its eventual use along a commercial smart-farming system. The array is comprised of four distinctive sensing devices based on nanoparticles, each functionalized with a different gas-absorbing polymeric layer. As discussed herein, the sensing array is able to identify as well as quantify three gas-analytes, two pesticide solutions, and relative humidity, which acts as a reference analyte. All of the evaluation experiments were conducted in close to real-life conditions; specifically, the sensors response towards the three analytes was tested in three relative humidity backgrounds while the effect of temperature was also considered. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analyzed using the common statistical analysis tool Principal Component Analysis (PCA). The sensing array, being compact, low-cost, and highly sensitive, can be easily integrated with pre-existing crop-monitoring solutions. Given that there are limited reports for effective pesticide gas-sensing solutions, the proposed gas-sensing technology would significantly upgrade the added-value of the integrated system, providing it with unique advantages.

The paper presents a theoretical framework and an empirical examination of the provision of port e-services through exploratory research. The presented survey examines the socio-technical environment in which port e-services are developed and provided, as well as the related investment decision making and evaluation procedures, in the Greek port sector. A number of hindering and enabling management and organisational factors are identified towards fully exploiting ICT capabilities and port e-services; our framework is deemed to be applicable to ports with similar geopolitical, economic, industrial and management characteristics to those of the examined organisations. The research outcome is a first step towards the provision of a systematic, unified framework for analysing and planning port e-services initiatives, in a socio-technical context.

The chapter describes DataBio’s pilot applications, led by NEUROPUBLIC S.A., for sustainable agricultural production in Greece. Initially, it introduces the main aspects that drive and motivate the execution of the pilot. The pilot set-up consisted of four (4) different locations, four (4) different crop types and three (3) different types of offered services. The technology pipeline was based on the exploitation of heterogeneous data and their transformation into facts and actionable advice fostering sustainable agricultural growth. The results of the pilot activities effectively showcased how smart farming methodologies can lead to a positive impact from an economical, environmental and societal perspective and achieve the ambitious goal to “produce more with less”. The chapter concludes with “how-to” guidelines and the pilot’s key findings.

This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions.