Artificial Intelligence-Based Approach for The Recommendations of Mango Supply Chain

Hamza Hussain; Hira Nazir; Muhammad SamiUllah; Muhammad Danial Faiz; Muhammad Adnan Faiz; Adnan Manzoor

Authors

Hamza Hussain Muhammad Nawaz Shareef University of Agriculture, Multan https://orcid.org/0009-0005-2306-5338
Hira Nazir Department of CyberSecurity, Emerson University, Multan
Muhammad SamiUllah Higher Education Department, Govt Graduate College of Commerce, Multan.
Muhammad Danial Faiz Department of Mathematics, Institute of Southern Punjab Multan
Muhammad Adnan Faiz Department of Business and public administration, Emerson University Multan,6400, Pakistan
Adnan Manzoor Department of Business and public administration, Emerson University Multan,6400, Pakistan

Keywords:

Artificial Intelligence (AI), Mango, Supply Chain Optimization, Agricultural Predictive Modeling

Abstract

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 an 80-20 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.

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