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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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基于图像的作物疾病检测与联合学习.

Denis Mamba Kabala1, Adel Hafiane2, Laurent Bobelin3

  • 1INSA CVL, University of Orleans, PRISME Laboratory EA 4229, 88 Boulevard Lahitolle, 18000, Bourges, France. denis.mamba_kabala@insa-cvl.fr.

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联合学习通过图像分析提高了作物疾病分类的准确性. 在这种联合学习方法中,ResNet50模型的性能优于Vision Transformers.

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科学领域:

  • 农业技术 农业技术
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 自动检测作物疾病对农业生产力和可持续性至关重要.
  • 集中式机器学习模型面临着数据隐私,可用性和传输成本方面的挑战.
  • 联合学习为这些挑战提供了分散的解决方案.

研究的目的:

  • 通过图像分析,探索联合学习对作物疾病分类的有效性.
  • 将卷积神经网络 (CNN) 和视觉转换器 (ViT) 模型在联合学习框架中的性能进行比较.
  • 确定作物疾病分类的最佳模型架构和联合学习参数.

主要方法:

  • 利用联合学习在PlantVillage开放访问图像数据集上训练CNN (ResNet50) 和ViT模型.
  • 研究了不同数量的学习者,交流轮和本地代对模型性能的影响.
  • 分析了与不同模型架构相关的计算时间和通信成本.

主要成果:

  • 联合学习模型的性能对学习者数量,通信轮次,本地代和数据质量都很敏感.
  • 在联合学习场景中,ResNet50在CNN模型中表现优越.
  • 视觉转换器 (ViT_B16,ViT_B32) 的计算时间较长,因此不太适合联合学习.

结论:

  • 联合学习是去中心化作物疾病分类的可行和有效方法.
  • 由于其性能和效率,ResNet50是一个非常适合在作物疾病检测中进行联合学习的模型.
  • 需要进一步的研究来优化联合学习策略,以加强作物疾病管理.