Artificial Intelligence-BasedApproach forThe Recommendations ofMango Supply Chain

This study utilizes a comprehensive dataset that encompasses variables reflecting temperature, humidity, precipitation, inventory levels, transportation modes, freshness scores, and ripeness scores. Compiled from various mango farms across different markets, this dataset provides a robust foundation for our analysis.To develop predictive models, we employed several machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests (RF), and Decision Trees (DT). We divided the dataset into training and testing sets, using an80-20 split for training and testing subsets, respectively.Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score. Our results indicate that Random Forests outperformed other models, achieving the highest accuracy, precision, recall, and F1 scores.A feature importance analysis revealed specific features that contributed significantly to the performance improvements of the model. These insights into feature importance can aid in refining the model's performance, making feature importance analysis a valuable component of model evaluation.