Enhancing Poultry Disease Classification Using Fecal Image: A Fusion Approach
Bibliographic record
Abstract
The rising demand for animal products is causing the agricultural sector, specifically poultry farming, to increase its production capacity.This increase in poultry farming can raise the risk of spreading contagious diseases like Newcastle, Coccidiosis, and Salmonella which may result in massive death rates among chickens as well as other serious economic losses.For detecting these diseases traditional techniques are labor-intensive, time-consuming, and expensive.Additionally, there are insufficient expertly trained professionals in rural areas.A deep learning-based model is proposed to detect early classification of these diseases using fecal images.The proposed model utilizes a hybrid architecture combining Efficient-NetV2B0 and DenseNet121 models for classification and achieves a high accuracy of 97.78% on the test set.Evaluation metrics including precision, recall, F1-score, confusion matrix, and ROC curve analysis illustrate the model's effectiveness in accurately categorizing poultry diseases.This approach offers a promising solution for early disease detection, enabling proactive health management in poultry farming to mitigate economic losses and safeguard human health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".