IoT monitoring of water consumption for irrigation systems using SEMMA methodology

The efficient use of water is an issue that has captured the attention of scientists, technicians, and the community at large. The sustainability of water resources has been threatened by the current imbalance between water supply and demand. Intelligent consumption of water would contribute to the balance and reduce the waste in applications such as the agriculture. This paper shows the design of a water consumption monitoring system based on the Internet of Things (IoT). With the implementation of this system could be known in real time the consumption of water in a crop. In addition, the user of the system may take corrective actions that optimize their water consumption; this is achieved by applying the SEMMA methodology to evaluate the data obtained by the system using two cluster algorithms, Simple K-means and GenClus++. With the application of SEMMA it was possible to determine periods of water consumption that were considered as waste in the irrigation of crops, applying data analysis with both algorithms

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Part 4: Fuzzy Algebra/SystemsInternational audienceThe increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations