Browsing by Author "Bati, C. T."

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Evaluation of Predictive Ability of Two Artificial Neural Network Algorithms and Multiple Regression Model for Meat Quality Traits Affected by Pre-Slaughter Factors
(Pakistan Agricultural Scientists Forum, 2021) Ser, G.; Bati, C. T.; Arik, E.; Karaca, S.
Recently, Artificial Neural Network (ANN) has been developed as an alternative to classical statistical methods in animal production. The methods can do classification or prediction by analyzing the information in the data set with the help of the neural network without requiring any preconditions (for example, distribution of data, non-linear data, highly correlated variables, etc.). In this context, we hypothesized that ANN, which is not only used in large and complex data sets but also estimates better in small data sets compared to classical statistical methods. The ability of ANN of Bayesian Regularization (BR-ANN) or Levenberg-Marquardt (LM-ANN) algorithms and Multiple Regression (MR) model to predict meat quality traits were assessed in a comparative study. The multilayer ANN algorithms obtained prediction data of meat quality measurements from pre-slaughter information using 1, 2, 4, 6 and 8 neurons in the hidden layer applied 10 times for each model. The performance of the methods was assessed according to the coefficient of determination (R-2) criteria, root mean squared error (RMSE) and residual prediction deviation (RPD). The comparison of the findings of BR-ANN and ML-ANN algorithms showed a similar ability to predict meat quality traits (error of prediction and R-2 values between 0.32-2.72 and 0.19-0.49, respectively). However, MR model predictions had lower performance than ANN algorithms, resulting in a wider error of prediction interval (0.4-3.44) and low R-2 (0.16-0.44). The RPD for meat quality traits was fair for BR-ANN and LM-ANN but was poor for MR. Based on our results, the ANN algorithms produced more reasonable prediction values than the MR model. ANN algorithms can be used as an acceptable alternative method for simple physical measurements of meat quality. ANN algorithms can be used reliably in small data sets.
Modelling Overdispersed Seed Germination Data: Xgboost's Performance
(Pakistan Agricultural Scientists Forum, 2023) Ser, G.; Bati, C. T.
Depending on the extent of variability in germination count data, the problem of overdispersion arises. This problem causes significant problems in estimation. In this study, gradient boosting algorithms are used as a new approach to support precision agriculture applications in estimating overdispersed germination counts. The database consisting of germination count data of weed (Amaranthus retroflexus L. and Chenopodium album L) and cultural plants (Beta vulgaris L. and Zea mays L.) with white cabbage seedlings, known for their allelochemical effects, was created. Accordingly, gradient boosting (GB) and extreme gradient boosting (Xgboost) algorithms were first developed for default values to estimate the germination counts of each plant; then, different combinations of hyperparameters were created to optimize the performance of the models. Root mean square error (RMSE), mean poisson deviation (MPD) and coefficient of determination (R2), were used as the statistical criteria for evaluating the performance of the above algorithms. According to the experimental results, the Xgboost algorithm showed superior performance compared to GB in both the default and hyperparameter combinations in the germination counts of A. retroflexus, C. album, B. vulgaris and Z. mays (RMSE: 0.725-2.506 and R2: 0.97-0.99). Our results indicate that the Xgboost made successful predictions of germination counts obtained under experimental conditions. Based on these results, we suggest the use of Xgboost optimal models for larger count data in precision agriculture.