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This paper introduces a novel drone-based plant disease detection system optimized for efficient and scalable deployment using MLOps. Utilizing the CADI AI dataset for cashew crop disease classification, it includes automated workflows for iterative training, testing, and deployment across YOLO architectures (YOLOv5, YOLOv8, YOLOv9, and YOLOv10). Advanced data augmentation and incremental dataset expansion, growing from 757 training images to the full dataset, ensure fair evaluations and model optimization. YOLOv5 achieved a peak mAP@50 of 59.4%, followed by YOLOv8 with 50.1%. Iterative fine-tuning revealed YOLOv9’s superior insect detection performance (mAP@50: 70.9%) and YOLOv10’s excellence in abiotic stress detection (mAP@50: 77.3%). This study highlights MLOps’ role in real-time model deployment and benchmarking, showcasing robust object detection capabilities and emphasizing iterative optimization and auto-deployment strategies to address dataset imbalance and enhance precision agriculture.

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Automated MLOps-Driven YOLO Framework for Drone-Based Plant Disease Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Salma Mosaad Mohamed Elfeky,
  • Mennaallah Nafady Ahmed Yehia,
  • Ali Hamdi

摘要

This paper introduces a novel drone-based plant disease detection system optimized for efficient and scalable deployment using MLOps. Utilizing the CADI AI dataset for cashew crop disease classification, it includes automated workflows for iterative training, testing, and deployment across YOLO architectures (YOLOv5, YOLOv8, YOLOv9, and YOLOv10). Advanced data augmentation and incremental dataset expansion, growing from 757 training images to the full dataset, ensure fair evaluations and model optimization. YOLOv5 achieved a peak mAP@50 of 59.4%, followed by YOLOv8 with 50.1%. Iterative fine-tuning revealed YOLOv9’s superior insect detection performance (mAP@50: 70.9%) and YOLOv10’s excellence in abiotic stress detection (mAP@50: 77.3%). This study highlights MLOps’ role in real-time model deployment and benchmarking, showcasing robust object detection capabilities and emphasizing iterative optimization and auto-deployment strategies to address dataset imbalance and enhance precision agriculture.