# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results
The most frequently used classification algorithms are selected such as Bayes network (BN), naive Bayes classifier (NBC), logistic (L), multilayer perceptron (MP), locally weighted learning (LWL), decision table (DTa), Hoeffding tree (HT), decision tree (DT), random tree (RT), and random forest (RF). The probabilistic-based algorithms BN (99.49%) and NBC (99.44%) give the highest accuracy for crop-rec, NBC (89.17%) for soy-dis, and the MP (94.29%) algorithm is the best method for the pla-spe dataset. The NBC and BN algorithms can achieve high accuracy because the data are high-dimensional and categorical. They perform efficiently when features are discrete and can be modeled using probability distributions. MP's performance depends on several factors, such as the complexity of the problem, the availability and size of the data, and appropriate hyperparameter tuning. MP gives the highest accuracy for the pla-spe data because it learns hierarchical representations of features from the data. This ability is beneficial when dealing with high-dimensional data or when the relevant features are not explicitly known. Table 3 shows an example of the obtained accuracy with some predefined configuration applied for all datasets [38]. Selecting the appropriate machine learning algorithms for agriculture datasets is an iterative process that requires understanding the problem, experimenting with various models, and fine-tuning them based on performance metrics. The key to choosing the most suitable algorithm lies in balancing model complexity, interpretability, and computational efficiency. Parameter tuning is crucial for optimizing machine learning algorithms' performance. The process involves systematically adjusting hyperparameters and evaluating their impact on performance metrics. In our work, it is essential to identify the most suitable \begin{table}
\begin{tabular}{c c c} \hline
**Feature** & **Set** \\ \hline mycelium & no, yes, N/A \\ \hline int discolor & none, brown, black, N/A \\ \hline scleroia & no, yes, N/A \\ \hline fruit-pods & normal, diseased, few-present, dna, N/A \\ \hline fruit spots & no, colored, brown-w, distort, dna, N/A \\ \hline seed & normal, abnormal, N/A \\ \hline mold-growth & no, yes, N/A \\ \hline seed-discolor & no, yes, N/A \\ \hline seed-size & normal, lt-norm, N/A \\ \hline shriveling & no, yes, N/A \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **crop-rec** & **soy-dis** & **pla-spe** \\ \hline Number of samples & 2200 & 1600 & 1985 \\ \hline Number of features & 7 & 64 & 35 \\ \hline Feature type & Numeric & Numeric & Numeric \\ \hline Data distribution & Right-skewed & Uniform & F \\ \hline Number of labels & 22 & 99 & 19 \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of agriculture data. configuration for a given dataset and task. Table 3 demonstrates the parameter list for chosen ML algorithms. The experiments show that rule-based algorithms exhibited strong performance across different datasets and parameter configurations, with the DT algorithm achieving the highest accuracy of 89.38%. The probabilistic and regression-based methods produced mixed results, with BN showing promise but needing careful parameter tuning. MP demonstrated robust performance, particularly when appropriately tuned, achieving high accuracy. Instance-based learning (LWL) had the longest training and testing times, indicating a high computational cost. Ensuring that preprocessing is sufficient for classification involves several steps and considerations to improve the quality of the input data, and thus, the performance of the classifier. In this work, the following steps are applied from raw data to training input data for agriculture data:  
* Noisy and mislabeled data are removed (smoothing, normalization). * Data augmentation techniques (rotation, shifting) are used to increase the diversity of the training set. * The dataset is balanced to ensure an equal number of records for each label.