Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach
Bibliographic record
Abstract
Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".