Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields

Abstract

Bird infestation is one of the major limiting factors affecting the production of indigenous rice in Nigeria. Conventional audio-based systems used in repelling birds deteriorate in effectiveness, cause noise pollution and consume excess power. This research therefore, developed an energy-efficient audio-based repellent system for rice fields incorporated with a convolutional neural network (CNN) model for bird detection. A CNN algorithm was developed using inception V3, built on transfer learning technique in google colab. The CNN model was trained with bird dataset of 275 bird species obtained from Kaggle database. The core of the system was the Raspberry pi 4, a low-powered microcomputer adopted to run the CNN model with information from a camera. The mechanical framework for the system was designed to support its sustainability in rice fields. The estimated coverage area was 42m2. The system used maximum power of 4.10W. The system supported multiple production of various sounds to prevent bird adaptability to fixed sounds, which could sustain its effectiveness. The incorporated camera aided in the detection of birds with consequent reduction in noise pollution inherent in conventional audio devices. The developed system used 53.52% of the average power reported for conventional audio systems. The developed audio-based repellent system is better in terms of bird detection, power consumption and less noise pollution. Further development on this system can be carried out with other detection algorithms which may require less power for processing.