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Olive cultivation has witnessed remarkable expansion globally, particularly in regions like the Mediterranean Basin and Morocco. However, olive plants encounter various threats from diseases and pests, such as bacterial blight, olive knot, peacock eye spot, and Aculus olearius infestations, posing risks to productivity and profitability. Traditional disease detection methods, including visual inspection and lab testing, are time-consuming and frequently prone to inaccuracies. In response, recent advancements in Artificial Intelligence (AI), particularly deep learning-based models like You Only Look Once (YOLO), offer promising avenues for early disease detection and management. This paper presents an advanced approach to detecting and identifying olive leaf diseases, focusing on peacock eye spot and Aculus olearius infestations, using the YOLOv8.1.21 object detection algorithm. Leveraging a dataset of 37,200 images sourced from in-field photography and public databases. The model categorizes olive leaves into healthy and infected categories, enabling early intervention and enhanced productivity in olive cultivation. This study highlights the importance of harnessing advanced AI in agriculture, particularly in olive cultivation. By achieving a remarkable mean Average Precision score of 99.5%, the developed detection method for olive leaf diseases holds promise for substantial improvements in plant productivity and profitability in the agricultural sector.

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A Deep Learning-Based Model for Efficient Olive Leaf Disease Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Hajar Hamdaoui,
  • Yassine Zarrouk,
  • Nour-Eddine Kouddane,
  • Youness Hsana,
  • Hanae AL Kaddouri,
  • Fadoua Chkird

摘要

Olive cultivation has witnessed remarkable expansion globally, particularly in regions like the Mediterranean Basin and Morocco. However, olive plants encounter various threats from diseases and pests, such as bacterial blight, olive knot, peacock eye spot, and Aculus olearius infestations, posing risks to productivity and profitability. Traditional disease detection methods, including visual inspection and lab testing, are time-consuming and frequently prone to inaccuracies. In response, recent advancements in Artificial Intelligence (AI), particularly deep learning-based models like You Only Look Once (YOLO), offer promising avenues for early disease detection and management. This paper presents an advanced approach to detecting and identifying olive leaf diseases, focusing on peacock eye spot and Aculus olearius infestations, using the YOLOv8.1.21 object detection algorithm. Leveraging a dataset of 37,200 images sourced from in-field photography and public databases. The model categorizes olive leaves into healthy and infected categories, enabling early intervention and enhanced productivity in olive cultivation. This study highlights the importance of harnessing advanced AI in agriculture, particularly in olive cultivation. By achieving a remarkable mean Average Precision score of 99.5%, the developed detection method for olive leaf diseases holds promise for substantial improvements in plant productivity and profitability in the agricultural sector.