# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 4 Proposed Methodology
* cross validation: Assesses the performance of a model by splitting the data into multiple subsets (folds), where each fold is used as a testing set exactly once. * tune hyper parameters: Optimizes the hyperparameters of the selected algorithm using the training set. * train model: Trains the model using the selected algorithm, training set, and tuned hyperparameters. * evaluate model: Assesses the trained model's performance using the testing set to measure its ability to generalize to unseen data. * calculate metrics: Computes performance metrics such as accuracy, precision, recall, and F1-score to quantify the model's success. * calculate average metrics: Computes the average performance metrics across all folds of cross-validation for each algorithm. Aggregates the performance metrics from each fold to provide a consolidated measure of the model's effectiveness. * log record: Logs the details of the modeling process, including the algorithm used, extracted features, performance metrics, and the split identifier. This log file (New Log Dataset), a product of experimenting with diverse datasets, algorithms, and configurations, is valuable for further study and analysis. An illustrative example of the log file, presented in CSV format, shows the feature vector composed of attributes with the highest weight on the accuracy of the results. The log file contains the dataset name, number of features, number of records, training rate, number of classes, feature type, data structure, accuracy, and algorithm name in the feature set. Indeed, drawing from our research and an exhaustive review of the current state of the art, we identified key factors significantly influencing the accuracy of ML classifier, for instance, the number of records, the number of features, the division of data between training and testing sets, and the specific model utilized. Each record of the CSV file encapsulates the experiment result of the application of a specific ML classifier with varying configurations, and the corresponding accuracy is documented. This CSV file subsequently served as the input for a proposed model designed to predict an estimated accuracy. This AI-trained model, in turn, plays a pivotal role in identifying the preliminary features represented that eventually optimize accuracy for a new agricultural case study. Figure 2 demonstrates the use of the proposed model on unseen agricultural data. By approximating results based on the pre-trained model's predictions, the process avoids the real-time execution of AI algorithms, instead utilizing historical data and tested dataset features to generate recommendations. This approach optimizes time consumption while ensuring the reliability of the suggested machine learning algorithms for analyzing agricultural datasets. Rather than deploying and fine-tuning an extensive array of algorithms, we approximate accuracy for appropriate ones, highlighting those with the highest precision. This approach significantly saves time, as suggestions are based on pre-trained AI models from previous agricultural data, providing efficient and reliable recommendations. No real-time solutions are utilized; instead, our suggestions are rooted in AI models trained on historical agricultural data. The illustration in Figure 3 outlines our approach, which comprises three distinct phases. Each phase's output serves as the input for the subsequent phase. In phase 1, collected diverse agricultural datasets are utilized and reorganized into a new dataset where multiple classifiers are applied in phase 2. Data are preprocessed by handling missing values, encoding categorical variables, and normalizing numerical features. After training and testing operations, the generated dataset with logged results is populated. These are used to train a new prediction model in phase 2. We populate the accuracy and AI algorithm classifiers for each dataset reorganization.