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智能植物疾病网:通过改进的优化方法,以物联网为基础的植物疾病检测提供了注意力机制的自适应密集混合卷积网络
N Ananthi1, V Balaji2, M Mohana1
1Department of Information Technology, Easwari Engineering College, Chennai, India.
Network (Bristol, England)
|February 24, 2024
概括
这项研究介绍了一种基于物联网 (IoT) 的新型智能农业模型,用于准确检测植物疾病. 该模型使用高级加密标准 (AES) 和具有注意力机制的自适应密集混合卷积网络 (ADHCN-AM) 来提高检测准确性.
科学领域:
- 农业技术 农业技术
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 植物疾病的增加导致农业的重大经济损失.
- 物联网 (IoT) 为智能农业解决方案提供了潜力.
- 植物疾病的自动检测对于有效的作物管理至关重要.
研究的目的:
- 引入一种利用物联网技术的新型植物疾病检测模型.
- 提高识别病态植物区域的准确性和效率.
- 与现有方法相比,证明模型的有效性.
主要方法:
- 图像采集和传输使用高级加密标准 (AES) 为安全.
- 使用面具区域与卷积神经网络 (R-CNN) 的图像预处理和细分.
- 通过具有注意力机制的自适应密集混合卷积网络 (ADHCN-AM) 检测植物疾病.
主要成果:
- 拟议的模型成功地从收集的图像中检测植物疾病.
- 通过ADHCN-AM方法,在疾病识别方面取得了很高的准确性.
- 在马修斯相关系数 (MCC) 中观察到95%的增强.
结论:
- 开发的基于物联网的模型为智能农业提供了有效的解决方案.
- 整合AES和ADHCN-AM显著改善了植物疾病的检测.
- 该模型表现出比当前方法更高的性能和有效性.
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