Sick and Dead Chicken Detection System Based on YOLO Algorithm
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Bibliographic record
Abstract
The poultry industry faces significant challenges in maintaining the health and welfare of chickens, with early detection of sick or dead birds being crucial for effective management and disease control. This paper presents a novel Sick and Dead Chicken Detection System leveraging the YOLO (You Only Look Once) algorithm, a state-of-the-art object detection framework. Our system employs YOLO's real-time image processing capabilities to identify and classify sick and deceased chickens from video feeds or images with high accuracy and speed. Currently chicken farmers are still unable to develop their farms to be able to keep up with increasing needs, this is due to the many chicken farming systems that have not been maximized in the development of their livestock systems, as one example is controlling sick chickens which are still being checked manually. system utilizes YOLO's real-time image processing capabilities to identify and classify sick and deceased chickens by paying attention to symptoms of disease including the movement of chickens by utilizing image processing with the YOLO algorithm, there are several stages in implementing YOLO, namely dataset collection and annotation, preprocessing, dataset division, label file creation, validation and hyperparameter setup, training and model application. We trained our model on a dataset comprising 435 annotated images of chickens exhibiting various health conditions. The proposed system enhances operational efficiency, minimizes human error, and supports timely interventions. Results indicate a significant improvement in detection accuracy and response time compared to traditional methods. The performance of the model applied using the confusion matrix method, so that good results are obtained by applying the YOLOv8 algorithm with an F1 rate of 94%, Precision 100%, Confidence 89.2%, Recall-Confidence of 100%, and Precision-Recall by 97% mAP@0.5. Each variable obtained an accuracy of 71.25% for dead chickens, 98.25% for sick chickens and healthy chickens.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it