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A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
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改善了玉米疾病识别的EfficientNet.

Jitong Cai1,2, Renyong Pan1,2, Jianwu Lin1,2

  • 1College of Big Data and Information Engineering, Guizhou University, Guiyang, China.

Frontiers in plant science
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

一个新的模型,FCA-EfficientNet,可以准确地实时识别玉米疾病,即使是复杂的背景. 这种轻量级的模型提供了高精度,提高了作物产量和农民收入.

关键词:
卷积神经网络是一个卷积神经网络.玉米叶病是玉米叶病的一种疾病.完全基于卷积的协调注意力.轻量级的模型轻量级的模型.真正的现场现场.

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科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 玉米病严重影响作物产量和质量,需要准确的实时识别.
  • 复杂的背景和疾病出现的变化挑战了现有的卷积神经网络模型.
  • 轻量级模型通常会损害疾病识别实时性能的准确性.

研究的目的:

  • 开发一个准确和高效的模型,在现实条件下识别玉米疾病.
  • 克服现有模型在处理复杂的背景和变化的局限性.
  • 为及时预防和控制疾病提供实用解决方案,提高作物产量和农民收入.

主要方法:

  • 拟议的FCA-EfficientNet,建立在EfficientNet架构上的基础上.
  • 集成了一个基于完全卷积的坐标注意模块,用于增强空间信息获取.
  • 采用自适应融合模块来整合多尺度图像信息并减少背景干扰.

主要成果:

  • 与其他深度学习模型相比,FCA-EfficientNet在准确性,精度,回忆和F1分数方面表现出色.
  • 该模型具有较低的参数数量 (3.44M) 和Flops (339.74M),性能优于大多数轻量级网络.
  • 在Android设备上实现了92.88ms的平均识别速度,满足了实时应用程序的要求.

结论:

  • 在现实的环境中,FCA-EfficientNet准确地识别了玉米疾病.
  • 该模型为农业应用提供了准确性和实时性能之间的实用平衡.
  • 有助于及时管理疾病,从而提高作物产量和农民的经济效益.