# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
Probabilistic methods are not successful when features are highly correlated, data are imbalanced, and there is a high number of outlier data in the input. Figure 4: Performance measurement comparison. Figure 5: Training times using different algorithms. Table 9 demonstrates the number of hidden layers, neurons per layer, learning rate, activation function, and number of epochs for each dataset. The accuracies are 99.17% on the crop-rec data, 86.82% on the pla-spe data, and 94.29% on the soy-dis data. The experimental results in Table 8 demonstrate that rule-based algorithms, particularly decision tree, random forest, and random tree, are highly effective for predicting and classifying agriculture data. These algorithms outperform probabilistic and regression-based methods significantly in terms of accuracy, TP rate, F-measure, and MCC. The high performance of rule-based algorithms suggests that they can effectively capture the complexities and patterns in the data, making them suitable for practical applications in agriculture data recommender system. Decision trees and random trees provide clear, interpretable rules for making predictions. This is important in agriculture, where stakeholders need to understand the reasoning behind recommendations. Random forests reduce the variance of the model and improve generalization to unseen data. This is particularly useful in agriculture, where the system must perform well under varied conditions. The agriculture  
Figure 6: Testing times using different algorithms. Figure 7: RAE percentage comparison. data in this work have many features. Rule based algorithms can handle high-dimensional data well and are capable of selecting the most relevant features during the training process. Overall, the ability of the decision tree, random forest, and random tree algorithms to capture complex relationships, provide interpretability, handle high-dimensional and non-linear data, and maintain robustness and efficiency makes them well-suited for agricultural recommendation systems. These characteristics help in delivering accurate, reliable, and actionable recommendations to improve agricultural productivity and management. The proposed recommendation system for agriculture faces the following limitations: \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Data** & **f1** & **f2** & **f3** & **f4** & **f5** & **f6** & **f7** & **f8** & **f9** & **f10** & **f11** & **f12** & **f13** \\ \hline crop-rec & 9 & 2125 & 67 & 1 & 22 & 1 & 1 & 1 & 0 & 1 & 0 & 99.49 & BN \\ \hline crop-rec & 7 & 1750 & 80 & 0 & 14 & 1 & 1 & 1 & 0 & 1 & 0 & 84.86 & RF \\ \hline crop-rec & 5 & 500 & 80 & 0 & 14 & 1 & 1 & 1 & 0 & 1 & 0 & 81.72 & RT \\ \hline soy-dis & 35 & 330 & 67 & 1 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 88.89 & BN \\ \hline soy-dis & 35 & 330 & 67 & 0 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 87.85 & HT \\ \hline soy-dis & 35 & 330 & 20 & 1 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 86.42 & BN \\ \hline soy-dis & 35 & 330 & 30 & 1 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 86.94 & MP \\ \hline soy-dis & 35 & 330 & 80 & 0 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 85.87 & RF \\ \hline soy-dis & 35 & 330 & 80 & 0 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 82.57 & RT \\ \hline soy-dis & 30 & 250 & 67 & 1 & 19 & 1 & 1 & 1 & 0 & 1 & 0 & 81.56 & BN \\ \hline soy-dis & 30 & 250 & 10 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 84.13 & BN \\ \hline soy-dis & 30 & 250 & 20 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 79.93 & BN \\ \hline soy-dis & 30 & 250 & 30 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 91.73 & BN \\ \hline soy-dis & 30 & 250 & 40 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 89.89 & BN \\ \hline soy-dis & 30 & 250 & 50 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 90.3 & NBC \\ \hline soy-dis & 30 & 250 & 80 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 71.06 & DTa \\ \hline soy-dis & 20 & 200 & 67 & 1 & 11 & 1 & 1 & 1 & 0 & 1 & 0 & 75.59 & L \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sample data from integrated ML united classification crop data. Figure 8: RRSE percentage comparison.