The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

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.

Soil and nutrient depletion from intensive use of land is a critical issue for food production. An understanding of whether the soil is adequately treated with appropriate crop management practices in real-time during production cycles could prevent soil erosion and the overuse of natural or artificial resources to keep the soil healthy and suitable for planting. Precision agriculture traditionally uses expensive techniques to monitor the health of soil and crops including images from satellites and airplanes. Recently there are several studies using drones and a multitude of sensors connected to farm machinery to observe and measure the health of soil and crops during planting and harvesting. This paper describes a real-time, in-situ agricultural internet of things (IoT) device designed to monitor the state of the soil and the environment. This device was designed to be compatible with open hardware and it is composed of temperature and humidity sensors (soil and environment), electrical conductivity of the soil and luminosity, Global Positioning System (GPS) and a ZigBee radio for data communication. The field trial involved soil testing and measurements of the local climate in Sao Paulo, Brazil. The measurements of soil temperature, humidity and conductivity are used to monitor soil conditions. The local climate data could be used to support decisions about irrigation and other activities related to crop health. On-going research includes methods to reduce the consumption of energy and increase the number of sensors. Future applications include the use of the IoT device to detect fire in crops, a common problem in sugar cane crops and the integration of the IoT device with irrigation management systems to improve water usage.

Goal: The main objective of this study is to analyze the impact of inserting a collaborative robot in a production line of a factory of the automotive sector. Design / Methodology / Approach: Two simulation environments were developed. The first one models the original operation without the collaborative robot. The second one evaluates the impact of inserting the robot. This work is quantitative. Results: The paper presents the use of digital manufacturing tools in a simulation of a collaborative operation between a human and a collaborative robot. The simulations with and without the collaborative robot were performed, and they make it possible to emphasize the benefits of the collaborative operation in a real production line. Limitations of the investigation: The first simulation scenario was validated from the real data provided by the factory. However, the second scenario is a suggestion, emphasizing the benefits of collaborative operation. Implementation of the second scenario was not conducted. Practical implications: Since its completion depended on support from the automotive sector, this work is noticeably practical. The real data used in the first scenario as well as the assumptions made in the second scenario allow one to conclude that it is possible to implement the propositions with the collaborative robot in the chosen line. Originality / Value: The use of the collaborative robot in Brazil was forbidden by regulation until recently. Even in other countries decision makers still find have difficulty deciding for this new technology. For this reason, the benefits of using collaborative robots, mainly in Brazilian companies, is still not clear. This work contributes to the collaborative robot discussion and consequently to Industry 4.0 implementation by creating a digital twin of an existing process and inserting a collaborative robot in it. The results should be used by decision makers to decide for inserting this technology in their factories.

This work, developed within SWAMP project, presents a conceptual model to analyze the variables that affect the adoption of Precision Agriculture technologies for smart water management in the context of Agriculture 4.0. Through the literature review a conceptual model is proposed that, based on the Internet of Things, Theory of Planned Behavior and Agriculture 4.0, allows predicting and explaining the studied behavior. The model allows analyzing the impact of several actions that can be implemented by the different actors operating in the farm ecosystem. From this model it is possible to define a data model that relates the measures associated to each identified variable, with focus on operations planning and irrigation scheduling. The proposed models show that IoT and Industry 4.0 methods can bring great improvements in planning, control and optimization of agricultural operations and smart water management.