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
Bibliographic record
Abstract
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 lowpower 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 learningbased 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 (8bit).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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".