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ApaltAI:一个基于网络的诊断系统,具有连续投票架构,用于检测梨果中的炭菌和

Mikjael Moreano¹, Angel Sosa¹, David Mauricio²

¹Faculty of Engineering, Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru.

Frontiers in plant science

|March 12, 2026

概括

这项研究介绍了一种自动化系统,用于识别使用新型深度学习架构 (VotingBS) 的梨疾病,如炭病和. 该系统实现了高精度,帮助农民在精准农业.

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

梨的生产面临着巨大的经济损失 (20-30%) 由于疾病,如炭病和.
准确及时识别疾病对于保持水果质量和产量至关重要.

研究的目的:

开发一种自动化系统,用于识别两种主要的梨疾病:炭病和病.
将二进制序列投票架构 (VotingBS) 与用于实际疾病诊断的网络应用程序集成.

主要方法:

实施一种分层的两阶段深度学习组合 (VotingBS) 用于疾病分类.
培训和验证使用674个标记梨水果图像的数据集.
开发一个带有作物管理和疾病诊断模块的网络应用程序.

主要成果:

投票BS系统实现了高性能指标:98.92%的精度,98.89%的回忆率和99.03%的准确性.
开发的系统显著超过了传统的疾病识别方法.
集成的网络应用程序增强了农民和农业机构的实际实用性.

结论:

混合深度学习模型与数字平台相结合,为植物疾病诊断提供了一种革命性的方法.
该系统促进高效,自动化和弹性精准农业实践.
这项技术可以显著减少梨生产中的经济损失.

关键词:

梨是一种梨.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.疾病检测检测疾病检测图像处理是图像处理的过程.

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