# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter List** \\ \hline BN & Maximum entropy, d separation \\ \hline NBC & Alpha is 0.3, binary/Boolean features \\ \hline L & Lipliner function, max iteration 240, tolerance is \(1\times 10^{-4}\) \\ \hline MP & 3 layers, sigmoid function, rate 0.01, 182 epoch, dropout rate is 0.2 \\ \hline LWL & Bandwidth is 0.6, Gaussian function, Chebyshev distance, cross-validation \\ \hline DTa & Threshold is 85\%, rule merging \\ \hline HT & Gini function, maximum depth is 6, delta is \(1\times 10^{-7}\), tau is 0.05 \\ \hline DT & Entropy function, random splitter, max leaf node is 8, balanced class weight \\ \hline RF & Maximum depth is 12, balanced class weight, impurity is 0.03, n estimator is 74 \\ \hline RT & Maximum depth is 9, balanced class weight, absolute error function \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter list for selected ML algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**TP Rate**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F-Measure**} \\ \hline  & **crop-rec** & **soy-dis** & **pla-spe** & **crop-rec** & **soy-dis** & **pla-spe** & **cop-dis** & **spe-rec** & **cop-res** & **soy-dis** & **pla-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 & 0.987 & 0.843 & 0.768 & 0.001 & 0.019 & 0.012 & 0.03 & 0.13 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: TP rate, precision, recall, and F-measure values for all datasets. better generalization, and consequently, lower RAE. The highest training time is 9.17 s, for the MP algorithm on the crop-rec data. LWL has the highest testing time for crop-rec. Neural network algorithms have a high potential to solve complex problems. On the other hand, they have high complexity in training because of the number of layers, forward and backward calculations, and data sizes. This problem can be reduced with hardware acceleration, batch processing, optimization, and data cleaning. Table 7 shows that it has the highest training cost of all datasets. However, the algorithm has one of the lowest testing costs for all datasets among the applied classification algorithms. The testing time is 0.01 s for the crop-rec data, 0.001 s for the soy-dis data, and 0.02 s for the pla-spe data. Probabilistic-based algorithms such as BN and NBC have efficient training and testing times. The random forest algorithm takes the longest time of the rule-based algorithms, as shown in Figures 4 and 5 [45]. In this paper, a new database is created using several crop data classification results. This dataset includes a data name, number of features, number of classes, number of records, classification method, and accuracy, as shown in Table 7. Several binary features are also added into the feature vector, such as multilabel, balanced, structures, time series, and binary. The data vector consists of 13 attributes and 720 records. Table 7 shows sample data from the newly developed dataset from ML-applied crop data.