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Updated: Jul 11, 2025

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自动葡萄叶营养缺陷疾病检测和分类 均衡优化器与深度转移学习模型
Vaishali Bajait1, Nandagopal Malarvizhi2
1Research Scholar, Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India.
概括
本研究引入了使用深度学习的自动葡萄叶病检测系统. 这种新的方法实现了高精度,优于现有的有效疾病分类方法.
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:
- 葡萄藤种植在全球范围内至关重要,但疾病显著影响产量和质量.
- 准确和早期发现葡萄叶病对于有效管理至关重要.
- 目前的疾病识别方法可能耗时,需要专家知识.
研究的目的:
- 开发和评估用于检测和分类葡萄叶病的自动化系统.
- 提高葡萄种植疾病诊断的准确性和效率.
- 为了提高农业疾病管理,利用深度学习.
主要方法:
- 图像预处理使用对比度有限的自适应直方体等分 (CLAHE) 和自适应双边过 (ABF).
- 通过SqueezeNet模型进行特征提取.
- 使用平衡优化器 (EO) 算法进行超参数优化.
- 通过堆叠自动编码器 (SAE) 模型进行分类.
主要成果:
- 拟议的平衡优化器-深度转移学习-葡萄叶疾病分类 (EODTL-GLDC) 技术实现了高精度.
- 在测试数据集上达到96.31%的精度,在培训数据集上达到96.88%的精度 (80:20分).
- 与其他深度学习和机器学习方法相比,表现出卓越的性能.
结论:
- 开发的自动化系统有效地检测和分类葡萄叶病.
- 整合SqueezeNet,EO和SAE模型为农业疾病诊断提供了一个强大的解决方案.
- 这种方法显示出在精密农业和疾病管理中实际应用的巨大潜力.
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