Location-Based Data Auditing for Precision Farming IoT Networks

Modern agriculture and farmers have been benefiting from the broad possibilities that wireless sensor networks and Internet of Things, in general, can bring to this primary sector and associated businesses. This new approach, also known as precision farming, exploits multiple high-end technologies, typically centered on environmental data sensing of diverse nature, e.g., temperature, humidity, moisture and COx, which can provide valuable indicators regarding the conditions where crops and livestock are produced. For that purpose, the respective sensors can be deployed in specific places in a farm, remaining static, or they can be placed on board of vehicles, such as tractors and unmanned aerial vehicles. Such sensors can also be integrated into wearable devices that are put on livestock individuals to track their positions, behavior and physiological aspects. Furthermore, this advanced technology-based approach to farming includes the respective data processing and analytics, in many cases employing machine learning and artificial intelligence techniques and algorithms, namely to provide the farmer with appropriate and reliable decision support systems. The work presented here focus specifically on a mobile application, which allows to collect inputs from users at the field and includes such human assessments in the automated loop of the sensing data flow in a precision farming system. The developed solution enables new functionalities on such systems, namely towards data auditing and labelling, that can be initiated directly from the field, which is where the sensors are and the respective sensing conditions can be assessed by the mobile app user.