The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.

Coffee is one of the leading worldwide drinks; therefore, it represents highly valued in the trade. However, coffee is a complex food from sowing to harvesting, processing, packaging, selling and consuming, Although coffee importance in most of its stages, no studies have analyzed the dynamics of global coffee research. This paper presents an analysis of the evolution of Coffee related international research. It is based on the renowned literature databases published by Scopus and Web of Science. The parameters studied included growth of publications, the main journals, countries, institutions, and an author keywords analysis according to their relationship with topics such as agronomy, health, economy, chemistry or biological compound, product and unclassified words. Interest in harvesting techniques and coffee side factors have been increasing through last years in an exponential trend. Producer and consumer countries have composed a synergy with their research interest, that allows stating an upcoming growing in techniques headed to the quality beverage. The contribution is to visualize state of the art in the area of coffee knowledge to generate trends to future researches. Key words: Agronomy; scientopy; countries.