# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 4 Proposed Methodology  
In the pursuit of optimizing agricultural data analysis techniques, the complex task of predicting and classifying agricultural data requires the efficient use of advanced machine learning and data analysis methodologies. The plethora of available algorithms necessitatesa judicious selection process, as the accuracy of predictions is profoundly linked to the choice of the model and its corresponding parameters. The task at hand is not only influenced by the nature of the data to be predicted and the dataset's features but also requires extensive experimentation with various artificial intelligence models to construct an effective predictive model. In this work, we aim to assist researchers in the field of agriculture to select suitable machine learning algorithms for their dataset. To achieve this, we employed historical agricultural data from different dataset sources, applying the most widely used artificial intelligence algorithms to each. Records in the log file were obtained from 723 sub-tables of the crop-rec, pla-spe, and soy-dis databases to increase the efficiency of the proposed model. For every algorithm, we varied the dataset utilization in terms of the division percentage between training and testing and the number of features participating in the classification, and the dataset size. Each trial underwent a comprehensive test, and we recorded its resulting accuracy, precision, recall, and F-measure. This meticulous process resulted in creating a comprehensive log file from numerous experiments, documenting the results and accuracies of each trial. Algorithm 1 represents the processes of building the proposed model, including a pre-analysis step to detect and overcome potential data limitations such as bias and missing data in agricultural datasets. This algorithm iterates over each dataset in the input agricultural datasets. For each dataset, it preprocesses the data and extracts features. It probes m different data splits, evaluates and experiments with various machine learning algorithms, performs cross-validation, tuning hyperparameters and training models, evaluating performance, calculating metrics, and logging the results. Indeed, the generated dataset contains the logged results, which include the performance metrics (accuracy, precision, recall, F1-score) for each dataset, algorithm, and data split combination. ```
1:Given agricultural datasets \(S\) (crop-rec,pla-spe,soy-dis), Feature set \(F\), Selected ML algorithms \(A\)
2:for each dataset in \(S\)do
3: dataset = preprocess data(dataset) {Encode categorical variables, normalize numerical features}
4:\(F\) = extract meta data features(dataset)
5:for\(i=1\) to \(m\) {m different data splits} do
6: training set, testing set = split data(dataset, \(i\), \(m\))
7:for each algorithm in \(A\)do
8:for\(j=1\) to \(k\) {k-fold cross-validation} do
9: hyper parameters = tune hyper parameters(algorithm, training set, j)
10: model = train model(algorithm, training set, hyper parameters, j)
11: performance = evaluate model(model, testing set)
12: metrics = calculate metrics(performance)
13:endfor
14: average metrics = calculate average metrics(all folds performance)
15: Log record(New log dataset, algorithm, \(F\), metrics, \(i\)-features)
16:endfor
17:endfor
18:endfor
19:Output: New Log Dataset[Use the generated dataset to train a new prediction model]
``` ```  
**Algorithm 1** AI powered agricultural modeling  
The parameters' descriptions are given below:  
* preprocess data: Prepares the dataset for model training by encoding categorical variables into numerical values and normalizing numerical features to a standard range (e.g., 0 to 1). * extract meta data features: This function extracts metadata features from the dataset, such as the number of records, the number and types of features, and labels. split data: Divides the dataset into training and testing sets based on the following training and testing ratios: (60,40), (65,35), (70,30), (75,25), (80,20), (85,15), (90,10).