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Highlights • The study provides a model of diffusion of ERP-based analytical insight platform. • The research focuses on the deep use of ERP on agricultural commodity producer farms. • This study combines quali-quantitative approaches on expert and farmers interviews. • This paper presents the importance of ERP impacts on the farm adoption stages. • The study presents the moderator impacts of Information Sharing (IS). Abstract Agriculture is a complex industry based on science. Agriculture relies on systems of climate-standard analysis: solar energy, heat, moist and systems for the area of field operation patterns: soil chemical composition, plant nutrition, genetic improvement, pest and disease control, harvesting. We believe that the new paradigm to increase efficiency in this segment goes through the adoption of enterprise resource planning (ERP) systems. Our empirical study is about the use and diffusion of ERP systems in a view of interoperability between different software packages with a view on business analysis functionality when taking a step further in farm management information system (FMIS). We hope this work can bring a theoretical and practical contribution for the agribusiness field and also increase debates about the platforms on cloud computer based on ERP, Enterprise 2.0 and Industry 4.0. The research presented in this study was carried out with 375 farmers in Brazil. The data gathering instrument used for the quantitative research was built based on the result of the qualitative (in-depth) study in combination with three theories: Diffusion of Innovation Theory (DOE), Technology-Organization-Environment Framework (TOE), and Inter-organizational Relations (IOR). The construct information sharing (IS) taken from IOR was applied to perform a moderator role on the measurement of ERP adoption stages. The results indicate the significant drivers for evaluation, adoption, and routinisation. Also, as a result, it was found that Information Sharing influence the relationship between evaluation and adoption positively. Moreover, the theoretical and managerial implications of the research results are also debated in the paper. [ABSTRACT FROM AUTHOR]

• Recognize operator's activities through a machine learning classifier. • Assess Vibration risk in real time to enhance operator's safety. • Develop a lightweight wearable device for real time monitoring. • Validate the process in real conditions through experimental tests. While being a fundamental driver of competitiveness in agroindustry, technological innovation has also introduced new critical elements related, for example, to the sustainability of the production processes as well as to the safety of workers. In such regard, the advent of the 4th industrial revolution (Agriculture 4.0) based on digitalization, is an unprecedented opportunity of rethinking the role of innovation in a new human-centric perspective. In particular, the establishment of an interconnected work environment and the augmentation of the operator's physical, sensorial, and cognitive capabilities, are two technologies which can be effectively employed for substantially improving the ergonomics and safety conditions on the workplace. This paper approaches such topic referring to the vibration risk, which is a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure of the operators to the activities performed. A miniaturized wearable device is employed to collect vibration data, and the signals obtained are segmented in time windows and processed in order to extract the significant features. Finally, a machine learning classifier has been developed to recognize the worker's activity and to evaluate the related exposure to vibration risks. To validate the methodology proposed, an experimental analysis in real operating conditions has been finally carried out by monitoring the activities performed by a team of workers during harvesting operations. The results obtained demonstrate the feasibility and the effectiveness of the methodology proposed. [ABSTRACT FROM AUTHOR]

• An Intelligent IoT precision irrigation approach was implemented at farm and district scales. • The precision irrigation system was made up of the integration of intelligent agents, crop models, agent-based modeling and Internet of Things technologies. • The behavior of each irrigation agent was influenced by their own interests and neighbors. • The proposed approach is promising for the design of distributed systems in agriculture. The fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs management. The aim of this paper is to present the design and implementation of an intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems. The study site was the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) located in Boyacá - Colombia, where water is distributed from the Chicamocha riverbed. In the proposed system, irrigation is supervised and controlled in each field by an intelligent irrigation agent that autonomously prescribes and applies water amounts with agronomical criteria. The methodology was applied with real (cyber-physical) and virtual (simulated) intelligent agents and was extended to eleven pump stations that supply water to 5911 fields. Using a MQTT protocol, hundreds of irrigation intelligent agents report water prescriptions and crop characteristics to a master agent in each pump station, who creates a regional irrigation map to manage georeferenced field information and performs negotiation of water resources between agents according to supply availability. Field maps and intelligent irrigation agents can be visualized using devices with internet access. Results demonstrated that irrigation amounts were correctly applied on the fields, thus improving the water use efficiency. This technology is a novel support to decision-making in water resources management applications at field and district scales. [ABSTRACT FROM AUTHOR]