Weight Prediction of Landlly Pigs from Morphometric Traits in Different Age Classes Using ANN and Non-Linear Regression Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Population, Data Collection and Management
2.2. Data Modelling
2.2.1. Artificial Neural Network Models
2.2.2. Non-Linear Regression Models
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Age | Trait * | R2 |
---|---|---|
4 weeks | HG | 0.8001 |
BL | 0.7616 | |
NCR | 0.7244 | |
PG | 0.7002 | |
HB | 0.6344 | |
6 weeks | BL | 0.8111 |
HG | 0.7871 | |
NCR | 0.7679 | |
PG | 0.7429 | |
HB | 0.7382 | |
8 weeks | HG | 0.8641 |
BL | 0.8601 | |
HB | 0.8008 | |
NCR | 0.7971 | |
PG | 0.7753 |
Correlation | 4 Week | 6 Week | 8 Week |
---|---|---|---|
HG-BL | 0.87 ± 0.03 | 0.92 ± 0.05 | 0.90 ± 0.03 |
BL-NCR | 0.83 ± 0.03 | 0.87 ± 0.05 | 0.87 ± 0.03 |
NCR-PG | 0.85 ± 0.03 | 0.89 ± 0.04 | 0.88 ± 0.03 |
PG-HB | 0.75 ± 0.04 | 0.83 ± 0.03 | 0.86 ± 0.03 |
HB-HG | 0.83 ± 0.03 | 0.87 ± 0.04 | 0.90 ± 0.03 |
HG-NCR | 0.89 ± 0.03 | 0.91 ± 0.05 | 0.91 ± 0.03 |
HG-PG | 0.91 ± 0.02 | 0.95 ± 0.04 | 0.93 ± 0.02 |
BL-PG | 0.81 ± 0.04 | 0.89 ± 0.04 | 0.87 ± 0.03 |
NCR-HB | 0.75 ± 0.04 | 0.83 ± 0.05 | 0.84 ± 0.03 |
BL-HB | 0.83 ± 0.03 | 0.90 ± 0.04 | 0.91 ± 0.03 |
4 Weeks | 6 Weeks | 8 Weeks | ||||||
---|---|---|---|---|---|---|---|---|
Combinations | Train MSE | Adj-R2 | Combinations | Train MSE | Adj-R2 | Combinations | Train MSE | Adj-R2 |
BL-HG-PG-HB-NCR | 0.3788 | 0.829 | BL-HG-PG-HB-NCR | 0.9927 | 0.849 | BL-HG-PG-HB-NCR | 1.3915 | 0.909 |
BL-HB | 0.6017 | 0.788 | BL-HB | 1.1551 | 0.821 | BL-HB | 1.8428 | 0.877 |
BL-HB-NCR | 0.4978 | 0.850 | BL-HB-NCR | 0.8957 | 0.849 | BL-HB-NCR | 1.5035 | 0.899 |
BL-HG | 0.4512 | 0.864 | BL-HG | 0.9626 | 0.830 | BL-HG | 1.4511 | 0.905 |
BL-HG-HB | 0.4361 | 0.823 | BL-HG-HB | 1.0488 | 0.836 | BL-HG-HB | 1.3856 | 0.906 |
BL-HG-HB-NCR | 0.4207 | 0.833 | BL-HG-HB-NCR | 0.8719 | 0.849 | BL-HG-HB-NCR | 1.2713 | 0.911 |
BL-HG-NCR | 0.4475 | 0.862 | BL-HG-NCR | 0.9909 | 0.845 | BL-HG-NCR | 1.3438 | 0.909 |
BL-HG-PG | 0.4419 | 0.867 | BL-HG-PG | 1.0231 | 0.830 | BL-HG-PG | 1.4508 | 0.904 |
BL-HG-PG-HB | 0.3635 | 0.724 | BL-HG-PG-HB | 1.0001 | 0.835 | BL-HG-PG-HB | 1.2949 | 0.905 |
BL-HG-PG-NCR | 0.446 | 0.867 | BL-HG-PG-NCR | 1.0037 | 0.843 | BL-HG-PG-NCR | 1.3149 | 0.908 |
BL-NCR | 0.4898 | 0.778 | BL-NCR | 0.9779 | 0.842 | BL-NCR | 1.6412 | 0.894 |
BL-PG | 0.4698 | 0.750 | BL-PG | 1.0477 | 0.823 | BL-PG | 1.6500 | 0.886 |
BL-PG-HB | 0.489 | 0.804 | BL-PG-HB | 0.9311 | 0.837 | BL-PG-HB | 1.6124 | 0.891 |
BL-PG-HB-NCR | 0.4699 | 0.857 | BL-PG-HB-NCR | 0.8993 | 0.848 | BL-PG-HB-NCR | 1.5419 | 0.902 |
BL-PG-NCR | 0.4799 | 0.836 | BL-PG-NCR | 0.9789 | 0.843 | BL-PG-NCR | 1.5384 | 0.898 |
HB-NCR | 0.5525 | 0.774 | HB-NCR | 1.0786 | 0.827 | HB-NCR | 1.9607 | 0.868 |
HG-HB | 0.4074 | 0.756 | HG-HB | 1.1194 | 0.820 | HG-HB | 1.6972 | 0.883 |
HG-HB-NCR | 0.9094 | 0.776 | HG-HB-NCR | 0.9789 | 0.838 | HG-HB-NCR | 1.5852 | 0.890 |
HG-NCR | 0.5011 | 0.826 | HG-NCR | 1.2344 | 0.811 | HG-NCR | 1.7502 | 0.875 |
HG-PG | 0.4984 | 0.806 | HG-PG | 1.3421 | 0.788 | HG-PG | 2.0297 | 0.864 |
HG-PG-HB | 0.5001 | 0.843 | HG-PG-HB | 1.1056 | 0.822 | HG-PG-HB | 1.7812 | 0.883 |
HG-PG-HB-NCR | 0.4473 | 0.813 | HG-PG-HB-NCR | 1.0774 | 0.836 | HG-PG-HB-NCR | 1.58000 | 0.891 |
HG-PG-NCR | 0.5038 | 0.839 | HG-PG-NCR | 1.1990 | 0.811 | HG-PG-NCR | 1.725 | 0.875 |
PG-HB | 0.6504 | 0.796 | PG-HB | 1.1592 | 0.807 | PG-HB | 2.2244 | 0.852 |
PG-HB-NCR | 0.8982 | 0.759 | PG-HB-NCR | 0.9818 | 0.833 | PG-HB-NCR | 1.9429 | 0.877 |
PG-NCR | 0.5695 | 0.704 | PG-NCR | 1.2357 | 0.793 | PG-NCR | 2.2363 | 0.844 |
Age | Prediction Model | Error Variation |
---|---|---|
4 weeks | ||
6 weeks | ||
8 weeks | ||
Combined |
Model | 4th Week a | 6th Week b | 8th Week b | Combined c | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | MSE | AIC | R2 | MSE | AIC | R2 | MSE | AIC | R2 | MSE | AIC | |
Linear regression | 0.834 | 0.436 | −94.08 | 0.853 | 0.936 | −0.13 | 0.908 | 1.388 | 47.55 | 0.900 | 1.243 | 83.81 |
Non-linear regression | 0.842 | 0.425 | −87.85 | 0.855 | 0.960 | 14.53 | 0.916 | 1.307 | 52.63 | 0.914 | 1.080 | 37.53 |
ANN | 0.869 | 0.441 | −87.20 | 0.852 | 0.871 | −2.73 | 0.913 | 1.271 | 43.05 | 0.913 | 1.004 | 14.48 |
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Preethi, A.L.; Tarafdar, A.; Ahmad, S.F.; Panda, S.; Tamilarasan, K.; Ruchay, A.; Gaur, G.K. Weight Prediction of Landlly Pigs from Morphometric Traits in Different Age Classes Using ANN and Non-Linear Regression Models. Agriculture 2023, 13, 362. https://doi.org/10.3390/agriculture13020362
Preethi AL, Tarafdar A, Ahmad SF, Panda S, Tamilarasan K, Ruchay A, Gaur GK. Weight Prediction of Landlly Pigs from Morphometric Traits in Different Age Classes Using ANN and Non-Linear Regression Models. Agriculture. 2023; 13(2):362. https://doi.org/10.3390/agriculture13020362
Chicago/Turabian StylePreethi, Andrew Latha, Ayon Tarafdar, Sheikh Firdous Ahmad, Snehasmita Panda, Kumar Tamilarasan, Alexey Ruchay, and Gyanendra Kumar Gaur. 2023. "Weight Prediction of Landlly Pigs from Morphometric Traits in Different Age Classes Using ANN and Non-Linear Regression Models" Agriculture 13, no. 2: 362. https://doi.org/10.3390/agriculture13020362