Irrigation Pivot-Center Connected At Low Cost For The Reduction Of Crop Water Requirements

Irrigation, particularly pivot-center, is widely used around the world to fill the need of crop watering. This method of irrigation has a low efficiency compared to other methods of irrigation such as drip systems and generally they use water without consider the real need of plants. In this paper we propose an automation system based on the Internet of Things (IoT), Geographic Information System (GIS) and quasi real-time in the cloud of water requirements to improve the efficiency of water use. Indeed, each segment of the pivot-center moves at a different speed compared to others; thus, must be individually controlled to optimize the yield of irrigation. Moreover, it necessary to integrate factors such as stage of crops’ development, heterogeneity of soil, runoff, drainage, soil components, nutrients and moisture content. In this paper we develop a complete system integrating sensors, GIS, Internet of Things and cloud computing. This approach allows to automate fine-grained the consumption of water without decreasing the yield. In addition to that, the collect of data and the soil moisture measurement will allow to adapt coefficient of evapotranspiration to local weather without having to resort to lysimetric measures. The proposed architecture allows to store and treat real-time, time series data and low-priority data such as 3D images used in digital phenotyping field which are treated with batch processing

Web Monitoring of Bee Health for Researchers and Beekeepers Based on the Internet of Things

peer reviewedThe Colony Collapse Disorder (CCD) also entitled ‘Colony Loss’ has a significant impact on the biodiversity, on the pollination of crops and on the profitability. The Internet of Things associated with cloud computing offers possibilities to collect and treat a wide range of data to monitor and follow the health status of the colon. The surveillance of the animals’ pollination by collecting data at large scale is an important issue in order to ensure their survival and pollination, which is mandatory for food production. Moreover, new network technologies like Low Power Wide Area (LPWAN) or 3GPP protocols and the appearance on the market easily programmable nodes allow to create, at low-cost, sensors and effectors for the Internet of Things. In this paper, we propose a technical solution easily replicable, based on accurate and affordable sensors and a cloud architecture to monitor and follow bees’ behavior. This solution provides a platform for researchers to better understand and measure the impacts factors which lead to the mass extinction of bees. The suggested model is also a digital and useful tool for beekeepers to better follow up with their beehives. It helps regularly inspect their hives to check the health of the colony. The massive collection of data opens new research for a better understanding of factors that influence the life of bees

Monitoring System Using Internet of Things For Potential Landslides

peer reviewedThe North-Western RIF of Morocco is considered as one of the most mountainous zone in the Middle East and North Africa. This area is more serious in the corridor faults region, where the recent reactivation of those tectonic layering may greatly contribute to the triggering of landslides. The consequences of this phenomenon can be enormous property damage and human casualties. Furthermore, this disaster can disrupt progress and destroy developmental efforts of government, and often pushing nations back by many years. In our previous works of Tetouan-Ras-Mazari region, we identified the areas that are prone to landslides by different methods like Weights of Evidence (WofE) and Logistic Regression (LR). In fact, these zones are built and susceptible. Undoubtedly, the challenge to save human lives is vital. For this reason, we develop a robust monitoring model as part of an alert system to evacuate populations in case of imminent danger risks. This model is ground-based remote monitoring system consist of more than just field sensors; they employ data acquisition units to record sensor measurements, automated data processing, and display of current conditions usually via the Internet of Things (IoT). To sum up, this paper outlines a new approach of monitoring to detect when hillslopes are primed for sliding and can provide early indications of rapid and catastrophic movement. It reports also continuous information from up-to-the-minute or real-time monitoring, provides prompt notification of landslide activities, advances our understanding of landslide behaviors, and enables more effective engineering and planning efforts

Edge Computing and Artificial Intelligence for Landslides Monitoring

peer reviewedLandslides are phenomena widely present around the world responsible each year of responsible for numerous loss of life and extensive property damage. Researchers have developed various methodologies to identify area of high susceptibility of landslide. However, these methodology can not can't predict when landslides are going to take place. Wireless Sensors Network, Internet of Thins and Artificial Intelligence offer the possibility to monitor in real-time parameters causing the triggering of rapid landslides. In this paper, we pave the way to a real-time monitoring of landslides in order to precociously alert in danger population by means of a warning system

A linear indexing approach to mass movements susceptibility mapping. The case of the Chefchaouen province (Morocco)

International audiencethe modelling of mass movements susceptibility (MMS) is essential for land-use planning and decision-making especially in the mountainous areas of Morocco. The main objective of this study is to create an MMS map for the Province of Chefchaouen in the north-western part of the Moroccan Kingdom using an index based approach (heuristic model). As a first step, mass movements (MM) features are identified in the study area by the interpretation of high-resolution remote-sensing data and from field investigations. Lithology, fault density, slope degree, slope aspect, elevation, stream density, land use, rainfall, and earthquakes equal-depths are identified as the main parameters controlling the occurrence of MM. Then, each parameter map is classified into a number of significant classes based on their relative influence for MM genesis. Rating values are assigned for each class, representing their influence on slope instability. The weights are extracted from real statistical data to reduce the subjectivity of the method. Finally, ROC (receiver operator characteristic) curves are drawn, and the areas below the curve (AUC) are measured to evaluate the degree of the model fit, and identify the best MMS map. This type of map can be valuable for general land-use planning, especially in Mediterranean mountainous areas