An Intelligent Management System and Advanced Analytics for Boosting Date Production

Shaymaa E. Sorour (), Munira Alsayyari, Norah Alqahtani, Kaznah Aldosery, Anfal Altaweel and Shahad Alzhrani
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Shaymaa E. Sorour: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Munira Alsayyari: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Norah Alqahtani: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Kaznah Aldosery: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Anfal Altaweel: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Shahad Alzhrani: Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Sustainability, 2025, vol. 17, issue 12, 1-24

Abstract: The date palm industry is a vital pillar of agricultural economies in arid and semi-arid regions; however, it remains vulnerable to challenges such as pest infestations, post-harvest diseases, and limited access to real-time monitoring tools. This study applied the baseline YOLOv11 model and its optimized variant, YOLOv11-Opt, to automate the detection, classification, and monitoring of date fruit varieties and disease-related defects. The models were trained on a curated dataset of real-world images collected in Saudi Arabia and enhanced through advanced data augmentation techniques, dynamic label assignment (SimOTA++), and extensive hyperparameter optimization. The experimental results demonstrated that YOLOv11-Opt significantly outperformed the baseline YOLOv11, achieving an overall classification accuracy of 99.04% for date types and 99.69% for disease detection, with ROC-AUC scores exceeding 99% in most cases. The optimized model effectively distinguished visually complex diseases, such as scale insert and dry date skin, across multiple date types, enabling high-resolution, real-time inference. Furthermore, a visual analytics dashboard was developed to support strategic decision-making by providing insights into production trends, disease prevalence, and varietal distribution. These findings underscore the value of integrating optimized deep learning architectures and visual analytics for intelligent, scalable, and sustainable precision agriculture.

Keywords: YOLOv11-Opt; date fruit classification; disease detection; fine-tuning; management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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