Real-time extensive livestock monitoring using lpwan smart wearable and infrastructure

Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest

Development of an open sensorized platform in a smart agriculture context: A vineyard support system for monitoring mildew disease

In recent years, some official reports, to produce best products regarding quality, quantity and economic conditions, recommend that the farming sector should benefit with new tools and techniques coming from Information and Communications Technology (ICT) realm. In this way, during last decade the deployment of sensing devices has increased considerably in the field of agriculture. This fact has led to a new concept called smart agriculture, and it contemplates activities such as field monitoring, which offer support to make decisions or perform actions, such as irrigation or fertilization. Apart from sensing devices, which use the Internet protocol to transfer data (Internet of Things), there are the so-called crop models, which are able to provide added value over the data provided by the sensors, with the aim of providing recommendations to farmers in decision-making and thus, increase the quality and quantity of their production. In this scenario, the current work uses a low-cost sensorized platform, capable of monitoring meteorological phenomena following the Internet of Things paradigm, with the goal to apply an alert disease model on the cultivation of the vine. The edge computing paradigm is used to achieve this objective; also our work follows some advances from GIScience to increase interoperability. An example of this platform has been deployed in a vineyard parcel located in the municipality of Vilafamés (Castelló Spain)