Over the last decade, the increased use of information and communication technology (ICT) in agriculture applications has led to the definition of the concept of precision farming or equivalently smart agriculture. In this respect, the latest progress in connectivity, automation, images analysis and artificial intelligence allow farmers to monitor all production phases and, due to the help of automatic procedures, determine better treatments for their farms. One of the main objectives of a smart agriculture system is to improve the yield of the field. From this point of view, the Internet of Things (IoT) paradigm plays a key role in precision farming applications due to the fact that the use of IoT sensors provides precise information about the health of the production. In this paper, the results of the recently concluded R&D project ENOTRIA TELLUS are reported. The project aimed at the development of all hardware/software components for implementing a precision farming architecture allowing the farmers to manage and monitor the vineyards’ health status. The smart architecture combines various sub-systems (web application, local controllers, unmanned aerial vehicles, multi-spectral cameras, weather sensors etc.) and electronic devices, each of them in charge of performing specific operations: remote data analysis, video processing for vegetation analysis, wireless data exchanges and weather and monitoring data evaluation. Two pilot sites were built where the smart architecture was tested and validated in real scenarios. Experimental activities show that the designed smart agriculture architecture allowed the farmers to properly schedule the various phases of cultivation and harvesting.

The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign.