PSI-16 Estimation of pigs live body weight from digital images using reference objects
Bibliographic record
Abstract
Abstract The live body weight (LBW) is an important parameter providing guidance for estimation of growth and feed conversion efficiency, body condition, presence of disease, and management of housing, nutrition and animal health in different life stages of livestock. This research study explores the possibility of developing a semi-automatic analytic system that estimates the LBW of pigs by applying machine learning methods that use approximated biometric measurements extracted from digital images acquired with consumer-level cameras in the presence of a reference object. Images corresponding to 12 pigs were sampled on two different dates 1 month apart and acquired using a consumer-level Motorola X4 mobile phone. The best 3 images for each pig were selected for each time point. Six measurements were extracted from each image using ImageJ. A total of 72 data points were analyzed using RStudio, and the generated correlation plot confirmed the positive correlations between the 6 predictors and LBW. Five machine learning (ML) methods were used to model the dependency between the measured parameters and LBW using WEKA. The Random Forest model outperformed all the other models and predicted LBW with the highest prediction accuracy (97%) and the lowest prediction error (MAE = 6.54), which could represent a good candidate for further studies. In conclusion, the semi-automatic image-based system is a promising approach combining machine learning, digital image analysis and manual scale extraction with the aid of reference objects of known size for accurate pig LBW estimation. The next stage of this study aims to integrate automatic image acquisition and image processing solutions in the current approach. This novel system will be cost- and time-efficient, and it can contribute to the development of intelligent solutions for scientific research and enhancing animal productivity in commercial pig farms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".