Sustainable Wastewater Management in Agriculture: A Deep Learning-based Olive Classification for Resource Efficiency in Water-Scarce Regions
Received: 21 February 2025 | Revised: 28 March 2025, 08 April 2025, and 10 April 2025 | Accepted: 12 April 2025 | Online: 16 May 2025
Corresponding author: Walid Karamti
Abstract
Efficient sustainable wastewater management combined with resource recovery represents a critical challenge for water-deprived areas, such as the Qassim region in Saudi Arabia. The effective integration of wastewater reuse into agricultural practices, particularly olive cultivation, requires advanced technological solutions to maximize yield, quality, and resource efficiency. This research investigates Deep Learning (DL)-based automatic olive classification systems as a vital component for optimizing post-harvest sorting operations, directly contributing to improved water efficiency by ensuring the appropriate allocation of wastewater resources to different olive varieties. The You Only Look Once (YOLO) object detection models—YOLOv5, YOLOv7, and YOLOv8—were employed to enhance accuracy and operational efficiency by classifying olives based on physical characteristics, including color, shape, and texture. A dataset of 2,025 images covering seven olive varieties was collected from publicly available sources, annotated using Roboflow, and preprocessed by resizing, rotation, scaling, and color adjustments. The YOLOv8 model achieved the best results, with a Recall of 99%, Precision of 99.1%, and mean Average Precision (mAP) of 99.4%, computed across thresholds [0.5:0.95] and [0.5:0.85]. These findings underscore the role of AI-powered classification in facilitating sustainable wastewater management, supporting more efficient water usage, and enhancing agricultural sustainability in arid regions.
Keywords:
ANNs, sustainable wastewater management, deep learning in agriculture, YOLO, Object Detection, olive classification, sorting, resource efficiency in water-scarce regionsDownloads
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