About the Project:
Smart Farming (SF) or precision agriculture constitutes a popular and effective Information and Communication Technology (ICT) platform by using efficient Internet of Things (IoT -enabled data monitoring and processing methods in farm fields. SF intends to digitalize the agriculture production in order to make it more profitable, viable, and sustainable. In this context, sMart fArming with dRoneS (MARS) aims to develop an efficient, integrated SF monitor system by using Unmanned Aerial Vehicles (UAVs), smart sensors, and meteorological stations to a) reduce the resources needed to cultivate crops and minimize environmental impact, b) detect potential anomalies (stress) in processing the farming inflow (products), and c) promote and advance local products of the region of Western Macedonia such as high-quality leguminous crops and fruits (peaches and apples).
Recent technological developments in ICT make it possible to combine many promising solutions in agriculture. For example, the Internet of Things (IoT) provides effective real-time data collection and processing mechanisms, while supporting data access and cloud decision-making mechanisms. The rapid development of Unmanned Aerial Vehicles (UAVs, drones) is a promising solution for real-time information gathering, as UAVs are now considered as one of the most user-friendly application technologies within the IoT umbrella thanks to their ability to directly connect and exchange data across any network and cloud infrastructure. UAVs are equipped with state-of-the-art sensors to collect soil, water, microclimate, tree foliage and smaller plant state information, and controlled by a management center without human intervention, except in the definition of flight data, are converted into self-propelled machines where by using photographic sensors, high-resolution cameras and thermal cameras capture the natural crop area in areas of visible and invisible spectrum, mapping altitudes, gradients in soil and plant height, identify areas with the spectral anomalies and generally record information necessary to achieve effective methods Smart Farming remotely, less expensive and accurately. The capture and recording of information is accompanied by a data processing system using Big Data, which enables risk assessment, forecasting of anomalies and classification of anomalies using stochastic and probabilistic methods.