Smart agriculture and Internet of Things (IoT) technology have become focal points for a wide range of intelligent decision-making applications to assist agriculture experts and farmers, particularly in crop problem management and control. Monitoring and managing storage conditions, particularly during lengthy storage, is a significant difficulty in grain storage such as cereals. As a result, there is an urgent need to employ innovative technologies for early identification and prevention of infestation propagation.

We built an IoT-based detection of weevils in cereals monitoring and detection prototype as a proof-of-concept that allows remotely monitoring cereals using smart agriculture sensors, actuators, and the internet for automatic detection of weevils in stored products, potentially decreasing the need for chemical control. Users can use web/mobile applications to interact with their grain silos and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer.

Moreover, we have collected data using accelerometer sensors, temperature sensor, and humidity sensor and we applied signal processing and statistical techniques to analyze collected data using machine learning and Internet of Things.