Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology
<|reference_start|>Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology: This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future.<|reference_end|>
@article{rahman2024advancing,
    title={Advancing Cucumber Disease Detection in Agriculture through Machine
  Vision and Drone Technology},
    author={Syada Tasfia Rahman, Nishat Vasker, Amir Khabbab Ahammed, Mahamudul
  Hasan},
    journal={arXiv preprint arXiv:2409.12350},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12350},
    primaryClass={cs.CV cs.AI}
}