LEDNet: Deep Learning-Based Ground Sensor Data Monitoring System

Abstract

Wireless Sensor Networks (WSN) are generally used for precision agriculture. However, reliability and cost are the key limitations of such approaches. In recent times, the application of Unmanned Aerial Vehicles (UAVs) in the agricultural field has become popular due to scalability, cost efficient and user friendly adaptations with the help of improved navigation algorithms. A Novel and cost effective Light Emitting Diode (LED) based wireless communication of field sensor data to server has been recently explored. This paper proposes a LEDNet framework that utilizes the LED pattern based sensor data encoding in an image with computer vision and deep learning based data extraction/communication of data. The LED pattern image can be captured using any decent resolution cameras that can be mounted on UAVs. The proposed framework generates LED sequences with the help of embedded boards and utilizes image processing and deep learning for the decoding of the sensor data from the LED pattern image. The experiments are carried out with the images taken under various lighting conditions from different heights in the field. Promising performance in terms of accuracy and power consumption is observed for the collection of sensor data using the proposed LEDNet framework for the LED bit sequence extraction from the dataset of images collected under various environmental conditions.