# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
* Data are shuffled to ensure a random distribution, which helps reduce bias during training. * The most relevant features are selected using correlation analysis. * New features are created from existing data using statistical methods (min, max, range). * Data are restructured into a consistent format. The number of features is reduced using feature selection methods to keep only the most relevant features. The data are split into training and testing sets. Evaluation metrics such as accuracy, precision, and the F1-score show the success of the data preprocessing. Kappa is a ratio that determines how accurate two raters are on classified items. As a case, it evaluates the reliability of multiple labels in data classification that divide the items into distinct classes. Kappa values are range between \(-1\) and \(+1\). A value close to \(+1\) is the perfect result in data classification. In this work, BN (0.994) and NBC (0.994) give the best kappa values in crop-rec, NBC (0.889) in soy-dis, and MP (0.935) and DTA (0.905) in pla-spe. Another evaluation metric for the classification model is the mean absolute error (MAE). This is a metric that averages the sum of all absolute differences between predicted values and observed values. In addition to that, MAE is a measure that indicates how much a particular model is distanced from the source data on average. If the MAE value is close to 0, it indicates perfect results. In the crop-rec data classification, both the probabilistic and rule-based algorithms have an MAE rate of less than 0.03. The experiments show  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Kappa**} & \multicolumn{3}{c}{**MAE**} & \multicolumn{3}{c}{**RMSE**} \\ \hline  & **crop-** & **soy-** & **pla-** & **crop-** & **soy-** & **pla-** & **crop-** & **soy-** & **pla-** & **crop-** & **soy-** & **pla-** \\  & **rec** & **dis** & **spe** & **rec** & **dis** & **spe** & **rec** & **dis** & **spe** & **rec** & **dis** & **spe** \\ \hline BN & 99.49 & 88.89 & 86.98 & 0.994 & 0.877 & 0.854 & 0.001 & 0.019 & 0.016 & 0.02 & 0.11 & 0.09 \\ \hline NBC & 99.44 & 89.17 & 83.75 & 0.994 & 0.889 & 0.829 & 0.001 & 0.014 & 0.011 & 0.02 & 0.11 & 0.11 \\ \hline L & 97.65 & 84.83 & 89.34 & 0.975 & 0.832 & 0.891 & 0.002 & 0.021 & 0.015 & 0.04 & 0.13 & 0.12 \\ \hline MP & 99.17 & 86.82 & 94.29 & 0.991 & 0.854 & 0.935 & 0.004 & 0.023 & 0.015 & 0.03 & 0.11 & 0.05 \\ \hline LWL & 79.33 & 57.49 & 83.41 & 0.783 & 0.527 & 0.827 & 0.075 & 0.098 & 0.082 & 0.18 & 0.22 & 0.19 \\ \hline DTA & 87.19 & 75.76 & 90.84 & 0.865 & 0.731 & 0.905 & 0.056 & 0.094 & 0.061 & 0.14 & 0.19 & 0.16 \\ \hline HT & 99.12 & 87.85 & 84.72 & 0.989 & 0.865 & 0.841 & 0.009 & 0.016 & 0.022 & 0.02 & 0.12 & 0.14 \\ \hline DT & 98.62 & 83.69 & 77.69 & 0.985 & 0.821 & 0.774 & 0.001 & 0.025 & 0.041 & 0.03 & 0.13 & 0.15 \\ \hline RF & 91.27 & 88.91 & 78.52 & 0.908 & 0.876 & 0.765 & 0.003 & 0.036 & 0.042 & 0.02 & 0.11 & 0.14 \\ \hline RT & 98.76 & 85.49 & 77.06 &that the LWL and DTa algorithms have higher error rates than the other algorithms for all three datasets. Model complexity and incomplete rules are reasons for the lower error rate in these algorithms. Another classification measure that has a lot of application in regression modeling is the Root Mean Squared Error (RMSE). It is similar to MAE except that it considers bigger disparities because it multiplies them by themselves [39; 40; 41]. \[k(kappa)=(A_{o}-A_{e})/(1-A_{e}) \tag{1}\]  
where \(A_{o}\) is the relative observed agreement and \(A_{e}\) the hypothetical probability of chance agreement after classification. \[MAE=\frac{\sum_{i=1}^{N}|y_{i}-x_{i}|}{n} \tag{2}\]  
where n is the total number of data, \(x_{i}\) is the total number of true values, and \(y_{i}\) is the prediction value. \[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2}}{n}} \tag{3}\]  
where \(x_{i}\) is the actual value, \(\hat{x}_{i}\) is the predicted value, and n is the total number of observations.