优化Wasserstein深度卷积生成对立网络培养了花生叶疾病识别系统
1Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Network (Bristol, England)
|July 2, 2024
概括
一个名为GLDI-WDCGAN-AOA的新系统使用优化Wasserstein深度卷积生成对抗网络和Aquila优化算法准确识别花生叶病. 这可以通过早期检测生和叶子斑等问题来提高产量和质量.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 地瓜叶病显著降低作物产量和质量.
- 准确和早期的疾病鉴定对于有效的管理至关重要.
研究的目的:
- 提出一种先进的系统,用于识别各种花生叶病.
- 为了提高核桃叶病检测的准确性和效率.
主要方法:
- 开发了使用优化瓦斯斯坦深度卷积生成对抗网络 (GLDI-WDCGAN-AOA) 的花生叶疾病识别系统.
- 雇员犹 模糊的语言双目标聚类 (HFL-BOC) 用于图像细分.
- 使用WDCGAN将图像分类为健康,早期叶子斑,晚期叶子斑,营养缺乏和生.
- 使用Aquila优化算法 (AOA) 优化WDCGAN参数.
主要成果:
- 与现有方法 (GLDI-DNN,GLDI-LWCNN,GLDI-CNN) 相比,GLDI-WDCGAN-AOA系统的准确性显著提高.
- 在各种疾病分类中显著降低了错误率.
- AOA优化导致WDCGAN性能改善,用于准确识别疾病.
结论:
- 拟议的GLDI-WDCGAN-AOA系统提供了一个高度准确和高效的解决方案,用于识别花生叶病.
- 这种方法有潜力减轻产量损失,改善地种植实践.
- WDCGAN和AOA的整合为农业疾病检测系统提供了一个有希望的方向.
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