一个混合型紧卷积变压器与双边过的咖啡疾病分类
Biniyam Mulugeta Abuhayi1, Andras Hajdu2
1Department of Information Technology, University of Gondar, Gondar P.O. Box 196, Ethiopia.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
一种新的紧卷积变压器 (CCT) 模型可以在阿拉伯咖啡中准确检测咖啡病 (CBD). 这种深度学习方法为可持续咖啡生产提供了灵敏而高效的解决方案.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由Collettotrichum kahawae引起的咖啡病 (CBD) 对全球阿拉伯咖啡生产构成重大威胁,导致大量的收成损失.
- 目前用于CBD检测的方法往往是主观和低效的,特别是在资源有限的农业环境中.
- 现有的深度学习研究主要集中在叶子疾病上,对果特异性感染如CBD的关注有限.
研究的目的:
- 开发一种轻量级,准确的深度学习模型,用于对健康的咖啡和受CBD影响的咖啡进行分类.
- 用先进的人工智能技术解决有关果特异性植物疾病检测的研究缺口.
- 为可持续咖啡种植提供实时,低资源部署的潜在解决方案.
主要方法:
- 1737张咖啡图像的数据集使用双边过和颜色细分进行了预处理.
- 紧卷积变压器 (CCT) 模型,结合卷积分支和变压器编码器,用于特征提取和分类.
- 该CCT模型与多层感知器 (MLP) 分类器集成,并使用早期停止和规范化技术进行了优化.
主要成果:
- 拟议的CCT模型实现了97.70%的验证精度和100%的CBD检测灵敏度.
- 通过CCT提取的特征与SVM (82.47%准确率,AUC 0.91) 和决策树 (82.76%准确率,AUC 0.86) 等传统分类器表现强.
- 与预训练模型相比,CCT系统表现出卓越的准确性 (97.5%),参数显著减少 (0.408万),训练时间更快 (2.3秒/时代).
结论:
- 开发的轻量级CCT模型为识别咖啡病提供了高度准确和灵敏的解决方案.
- 该模型的效率和低资源需求使其适用于可持续咖啡生产的实时应用.
- 这项研究强调了先进的深度学习架构在解决农业植物病理学的关键挑战方面的潜力.
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