Recognition of the Sound of the Lonchura Maja Bird and the Threat of House Sparrows Using Edge Impulses Based on a Custom Deep Neural Network to Protect Rice Plants

• Published in: Ingénierie des systèmes d'Information 2024-10, Vol.29 (5), p.1755-1762
• Main Authors: Rachmad, Aeri, Setiawan, Eko, Hasbullah, Abdul Wahib
• Format: Article
• Language: eng ; fre
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Background noise; Datasets; Flash memory (computers); Machine learning; Microcontrollers; Neural networks; Random access memory; Recognition; Software; Tensors
• ISSN: 1633-1311; 2116-7125
• DOI: 10.18280/isi.290509

The presence of birds can be used as a biological indicator related to the quality of environmental health in development. However, the presence of pest birds is a threat to farmers. This paper employs edge machine learning regarding audio recognition of birds Lonchura Maja and the sound of birds of house sparrow, which can be applied to a low-power microcontroller. We also train another nearby bird sound of turtledove, which is often seen around the rice fields on Bangkalan, to act as noise or background sound; we test the reliability of four machine learning (ML) algorithms, then embed them in the microcontroller RP2040 and connect. The first machine learning model is a custom deep neural network (CNN) 1D with two layers, and the second model uses transfer learning-based architecture. The Edge Impulse embedded platform learning machine is used to conduct training and testing. The resulting learning model was then implemented as an Arduino Library, as an Unoptimized float (32-bit) and Optimized integer quantization (8-bit). The estimated values produced by the microcontroller are evaluated in 4 cases, using the EON compiler and Tensor Flow Lite. In this paper, the custom 1D CNN model provides the best accuracy value, with 87.4% accuracy during training and 84.59% accuracy on testing, and it uses very efficient resources, 66.2 Kbyte Flash memory and 11.8 Kbyte Peak RAM.