This paper presents a digital twin in the agriculture domain by leveraging the technologies developed by Sensing Change and the Smart Water Management Platform projects. The Sensing Change project developed a soil probe whereas the SWAMP project is currently developing an Internet of Things platform for water management in farms. This paper leverages the technologies developed by those projects by building an initial digital environment to create a cyber-physical-system (CPS) so farmers can better understand the state of their farms regarding the use of resources and equipment. We conclude that our system can gather data from the soil probe and display its information in a dashboard which enables for further deployment of more soil probes and other monitoring and controlling devices to create a fully operating digital twin.

User demographic and behavior data obtained from real user observation provide valuable information for designers. Yet, such information can be misinterpreted if presented as statistic figures. Personas are fictitious user representations created in order to embody behaviors and motivations that a group of real users might express, representing them during the project development process. This article describes the persona as being an effective tool to the users' descriptive model.

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection.

In this paper, a digital image processing methodology based on thermal images to detect and locate water leak in underground pipes will be presented. With the advent of smart cities, some countries began to face this problem with technologies, such as Smart Water Grids. However, with this proposal it is intended to contribute with the smart cities context but with a nondestructive and less complex system. Also, it is proposed in this paper the use of the q-sigmoid function that is an alternative to pre-processing step to digital image processing. The potential of this function will be validated as method to contrast enhancement and to highlight regions of interest. The proposal was applied in thermal images captured from the soil surface with an underground water leak. Such conditions were proposed into a laboratory, using an ideal model with sandy soil. The results obtained from this experiment were promising, since it was able to detect leaks at early stages of the experiment, which suggest a potential use of the methodology and the proposed preprocessing technique to detect water leaks.

Agile project management have gained substantial popularity in the IT sector over the years, particularly among software development organizations. The attainment of success in these endeavors hinges on various factors known as “Critical Success Factors” (CSFs), a subject frequently explored in academia. Nonetheless, there is no consensus regarding their impact on Agile software development. To address this gap, this study aims to recognize and analyze CSFs in software development projects executed using Agile methodologies. The study identified 25 CSFs categorized into five categories based on recent literature findings. A structured survey with 7-point Likert scale questionnaires was conducted among Agile software development professionals, resulting in 121 valid responses. These responses were analyzed using Structural Equation Modeling (PLS-SEM). The findings highlight that all constructs formed within the CSFs significantly contribute to the success of Agile software development projects. Moreover, the study reveals that the positive effects of these CSFs on success follow a descending order: Project Factors, People Factors, Organizational Factors, Process Factors, and Cultural Factors. Intriguingly, when these CSFs are considered as a second-order construct, their impact is even more pronounced, suggesting a synergistic relationship among these elements. This research addresses a crucial scientific aspect in project management literature and also introduces a novel yet theoretically grounded model. The study demonstrates strong validity, reliability, and explanatory capabilities (56.1%) concerning the phenomenon. Additionally, it contributes to the discussion on CSFs by pinpointing statistically significant factors and gauging their individual and collective influence on the success of Agile software development projects.