From Data to Insight: Machine Learning Approaches for Fish Age Prediction in European Hake
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Sampling Methodology
Otolith Extraction Process and Imaging
2.2. Otolith Morphometry and Age Classification
2.3. Statistical Analysis
2.4. Description of ML Algorithms
2.5. Machine Learning (ML)
2.6. Machine Learning Techniques
2.6.1. Stochastic Gradient Descent
2.6.2. Gradient Boosting
2.6.3. Adaptive Boosting
2.6.4. Random Forest
2.6.5. Decision Trees
2.7. Assessment of Model Performance
2.7.1. Mean Square Error (MSE)
2.7.2. Root Mean Square Error (RMSE)
2.7.3. Mean Absolute Error (MAE)
2.7.4. Mean Absolute Percentage Error (MAPE)
2.7.5. Coefficient of Determination (R2)
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Otolith Mophometric Characters | Formula | Reference |
---|---|---|
rectangularity (R) | [59] | |
squareness (S) | [60] | |
ellipticity (E) | [61] | |
roundness (RO) | [61] | |
aspect ratio (AR) | [62] | |
form factor (F) | [61] | |
circularity (C) | [59] |
Variable | N | Mean | Median | SD | Min | Max |
---|---|---|---|---|---|---|
Age | 150 | 2.89 | 3.00 | 1.58 | 1 | 9 |
Total length (cm) | 150 | 28.23 | 27.73 | 6.26 | 17.15 | 47.60 |
Total weight (g) | 150 | 178.05 | 136.50 | 138.72 | 32.28 | 804.5 |
Otolith weight (g) | 300 | 0.10 | 0.09 | 0.05 | 0.03 | 0.30 |
Otolith perimeter (P) | 300 | 37.83 | 35.83 | 9.74 | 21.41 | 82.68 |
Otolith area (A) | 300 | 51.34 | 47.95 | 21.45 | 19.26 | 121.12 |
Otolith length (D) | 300 | 13.31 | 13.15 | 2.93 | 8.13 | 21.22 |
Otolith width (OW) | 300 | 5.44 | 5.46 | 1.09 | 3.32 | 8.84 |
Form factor | 300 | 0.45 | 0.45 | 0.07 | 0.17 | 0.58 |
Circularity | 300 | 29.06 | 28.05 | 6.24 | 21.58 | 71.83 |
Roundness | 300 | 0.36 | 0.36 | 0.02 | 0.29 | 0.40 |
Rectangularity | 300 | 20.90 | 19.96 | 8.32 | 7.87 | 50.24 |
Aspect ratio | 300 | 2.44 | 2.43 | 0.11 | 2.20 | 2.77 |
Ellipticity | 300 | 155.06 | 142.03 | 71.09 | 54.71 | 386.98 |
Squareness | 300 | 0.68 | 0.69 | 0.03 | 0.58 | 0.74 |
Source | Logworth | p Value | VIF | |
---|---|---|---|---|
Total population (R2 = 91.0%) | ||||
Otolith weight | 137.362 | <0.0001 | 1.19 | |
Form factor | 3.719 | 0.0002 | 1.28 | |
Aspect ratio | 2.202 | 0.0063 | 1.29 | |
Males (R2 = 91.6%) | ||||
Otolith width | 7.820 | <0.0001 | 3.34 | |
Aspect ratio | 4.014 | 0.00010 | 3.20 | |
Otolith weight | 2.149 | 0.00710 | 1.08 | |
Females (R2 = 95.8%) | ||||
Otolith weight | 52.600 | <0.0001 | 1.00 |
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Klaoudatos, D.; Vlachou, M.; Theocharis, A. From Data to Insight: Machine Learning Approaches for Fish Age Prediction in European Hake. J. Mar. Sci. Eng. 2024, 12, 1466. https://doi.org/10.3390/jmse12091466
Klaoudatos D, Vlachou M, Theocharis A. From Data to Insight: Machine Learning Approaches for Fish Age Prediction in European Hake. Journal of Marine Science and Engineering. 2024; 12(9):1466. https://doi.org/10.3390/jmse12091466
Chicago/Turabian StyleKlaoudatos, Dimitris, Maria Vlachou, and Alexandros Theocharis. 2024. "From Data to Insight: Machine Learning Approaches for Fish Age Prediction in European Hake" Journal of Marine Science and Engineering 12, no. 9: 1466. https://doi.org/10.3390/jmse12091466