# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
All data in this table were obtained after the classification of the dataset in the Weka software version 3.6.12. Weka is a widely used machine learning tool developed by the University of Waikato in New Zealand. It includes data preprocessing, classification, clustering, and visualization. Data vector = {data name, f1 = #of features, f2 = #of records, f3 = training rate, f4 = classification/regression, f5 = #of class, f6 = feature type, f7 = balanced/unbalanced, f8 = binary, f9 = multilabel, f10 = structured, f11 = time series, f12 = accuracy, f13 = ML method}. The experimental results show that the best methodology and data format can be predicted using machine learning algorithms, as shown in Table 8. The results show that rule-based algorithms give promising results in predicting the best machine learning algorithm and data structure for crop data. Table 8 shows accuracy, kappa, MAE, RAE, TP rate, F-measure, and MCC values for the integrated new datasets after individual machine learning processing. The results show that the decision tree (89,38%), random forest (87.61%), and random tree (84.27%) algorithms have the highest accuracy for the proposed data selection. There are several reasons behind the rule-based algorithms' success. These algorithms can handle both categorical and numerical data. They can naturally handle a mix of data types without requiring extensive preprocessing, making them suitable for \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**FP Rate**} & \multicolumn{3}{c}{**MCC**} & \multicolumn{3}{c}{**ROC Area**} & \multicolumn{3}{c}{**PRC Area**} \\ \hline \multirow{3}{*}{**crop-rec**} & **soy-dis** & **pla-spe** & **crop-rec** & **soy-dis** & **pla-spe** & **crop-rec** & **soy-dis** & **pla-spe** & **crop-rec** & **soy-dis** & **pla-spe** \\ \hline BN & 0.001 & 0.014 & 0.011 & 0.994 & 0.877 & 0.885 & 1.000 & 0.983 & 0.992 & 0.999 & 0.914 & 0.923 \\ \hline NBC & 0.001 & 0.015 & 0.009 & 0.993 & 0.890 & 0.898 & 1.000 & 0.992 & 0.952 & 0.998 & 0.952 & 0.961 \\ \hline L & 0.002 & 0.020 & 0.014 & 0.976 & 0.833 & 0.841 & 1.000 & 0.985 & 0.994 & 0.994 & 0.928 & 0.937 \\ \hline MP & 0.001 & 0.015 & 0.012 & 0.992 & 0.847 & 0.855 & 1.000 & 0.981 & 0.990 & 0.999 & 0.907 & 0.916 \\ \hline LWL & 0.009 & 0.055 & 0.041 & 0.757 & 0.591 & 0.827 & 0.990 & 0.950 & 0.928 & 0.852 & 0.833 & 0.982 \\ \hline DTa & 0.005 & 0.024 & 0.019 & 0.883 & 0.812 & 0.730 & 0.989 & 0.941 & 0.846 & 0.885 & 0.761 & 0.684 \\ \hline HT & 0.001 & 0.018 & 0.009 & 0.994 & 0.876 & 0.788 & 1.000 & 0.994 & 0.894 & 0.999 & 0.966 & 0.869 \\ \hline DT & 0.001 & 0.019 & 0.022 & 0.986 & 0.829 & 0.746 & 0.998 & 0.973 & 0.875 & 0.987 & 0.809 & 0.728 \\ \hline RF & 0.001 & 0.015 & 0.028 & 0.994 & 0.879 & 0.791 & 1.000 & 0.993 & 0.893 & 1.000 & 0.959 & 0.863 \\ \hline RT & 0.002 & 0.015 & 0.023 & 0.987 & 0.849 & 0.764 & 0.994 & 0.921 & 0.828 & 0.977 & 0.770 & 0.693 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: FP rate, MCC, ROC, and PRC area rates for all datasets. datasets with diverse feature types. All rule-based algorithms are suitable for both binary and multiclass classification problems. They can naturally handle multiple classes without significant modifications. In addition to that, decision tree and random tree methods can handle missing values in the features without requiring imputation. Rule-based algorithms also have some limitations, such as overfitting and sensitivity to noise. On the other hand, they are less sensitive to outliers and missing values than the other classification algorithms. They can use non-linear relationships between features and the target variable. Rule-based algorithms assign low importance to irrelevant or redundant features during the training process. The TP rates are more than 90% in the MP, LWL, Dta, DT, RF, and RT algorithms. Probabilistic and regression-based methods do not perform well in this newly developed dataset for prediction and classification.