# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
\[RAE=\frac{\sum_{i=1}^{N}|x_{i}-t_{i}|}{\sum_{i=1}^{N}|t_{i}-\hat{t}_{i}|} \tag{4}\]  
\[RRSE=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-t_{i})^{2}}{\sum_{i=1}^{N}(t_{i}-\hat{t })^{2}}} \tag{5}\]  
where \(x_{i}\) is predicted, \(t_{i}\) is target, and \(\hat{t}_{i}\) is the average value of the target data. Table 3 demonstrates the TP rate, precision, recall, and F-measure values. The TP rates are more than 90% for all methods except the LWL and decision table methods. In general, the LWL method has the lowest rates for all datasets. The true positive (TP) rate refers to the ratio of real positive cases that can be correctly identified. In many cases, it is critical, especially when correctly classifying positives, especially in medical diagnoses. The true positive rate is high in the BN and NBC algorithms for the crop-rec data, meaning that the model is effectively identifying and correctly classifying a large proportion of the positive instances in the dataset. On the other hand, LWL (79.33) on the crop-rec data and the rule-based algorithms on the soy-dis and pla-spe datasets have low TP rates, which shows a low rate of accuracy. A false positive (FP) appears when the model wrongly predicts a positive outcome and the true outcome is the opposite. There should be a balance between TPs and FPs. Our aim in this work is to increase the TP and decrease the FP rates. In medical diagnosis, TP values are very critical, they might affect the patient's health critically. In agriculture work, the expectation is a balance between TPs and FPs. In machine learning, especially in classification, precision is used as a measure of the accuracy of positive predictions from the model. High precision indicates that whenever the model predicts a positive item classification. The recall measurement is useful, especially where false negative results are expensive and undesired. A single value combining precision and recall is called the F-measure or F1-score. We can use the F-measure only in classification. It is a metric that helps us to find the optimal point in this trade-off [42; 43; 44]. The results given in Tables 3-6 are used as an attribute in the proposed data model for the farmer recommendation system. Table 4 shows the FP rate, MCC, ROC area, and PRC area values for all the algorithms. The precision-recall curve (PRC) is another evaluation tool for binary classification models. It is useful when dealing with imbalanced datasets such as the data used in this work. The ROC area rate considers all possible classification thresholds. If the ROC value is close to +1, the classification model is reliable; otherwise it is not. In these experiments, all ML models have a value larger than 0.8, which shows that they are all appropriate models to apply to these datasets. High precision and accuracy and low error values indicate the success of the proposed models. Figures 4-8 shows training time, testing time, root absolute error (RAE), and root relative squared error (RRSE) values for each dataset. The time required for training and testing classification algorithms can vary significantly based on multiple factors, including the complexity of the algorithm, the size and nature of the dataset, and the computational resources available. In Figures 4 and 5, our experiments show that algorithms such as L, MP, and LWL take more training time than others. LWL has the highest time for testing time; other algorithms, including MP, have a low cost by taking a shorter time on all datasets. The RAE is a way to assess the accuracy of predictions, with lower values indicating better performance. The NBC and HT algorithms on crop-res, NBC and HT on soy-dis, and NBC and HT on pla-spe give the best RAE values. HT algorithms are modeled to adapt quickly to changes in the data distribution. The simplicity of the HT model can contribute to