一个零射击的学习框架,用于叶病检测,分类和严重程度估计,使用对比的图像文本表示.
Shiva Shankar Annaram1, G Gopichand2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
这项研究引入了一种新的零射击双编码框架,用于识别叶疾病,包括未见的类型,并评估其严重程度. 先进的模型实现了高精度,改善了实时农业疾病监测,不需要再培训.
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:
- 迟迟发现叶病会影响农业生产率.
- 传统的深度学习模型与新的或新出现的疾病模式作斗争.
- 手动检查和模型再培训是目前的局限性.
研究的目的:
- 开发一个框架来识别未见的叶疾病.
- 用损伤面积比率来估计疾病的严重程度.
- 改善实时农业疾病监测.
主要方法:
- 提出了一个零射击双编码器框架,集成视觉变压器 (ViT) 和基于RoBERTa的语义编码器.
- 在各种环境条件下使用精选的叶数据集.
- 评估模型在识别未见疾病类别和估计严重程度方面的表现.
主要成果:
- 实现了98.7%的准确性,98.0%的精度和98.0%的回忆.
- 超过了现有的五种最先进的方法.
- 证明了增强的识别准确性和可扩展性.
结论:
- 零射击双编码器框架有效地识别了未见的叶疾病,并估计了严重程度.
- 该模型为实时农业疾病监测提供了一个可扩展的解决方案.
- 对于新的或新出现的疾病模式,不需要重新培训.
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