混合功能优化了CNN对大米作物疾病预测的预测
S Vijayan1, Chiranji Lal Chowdhary2
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|March 6, 2025
概括
本研究引入了一种混合WOA_APSO算法,用于使用卷积神经网络 (CNN) 准确检测病. 这种新方法显著提高了疾病分类的准确性,有助于农业的可持续性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 自主系统在农业中至关重要,用于检测水疾病,以防止产量损失.
- 目前的疾病识别方法在准确性和计算效率方面扎.
- 精确的叶片细分和疾病阶段分析是关键的挑战.
研究的目的:
- 开发一种更准确,更具成本效益,更可靠的米病检测方法.
- 引入一种混合生物灵感算法 (混合WOA_APSO) 来优化特征选择.
- 使用卷积神经网络 (CNN) 增强大米疾病分类.
主要方法:
- 提出了一种混合WOA_APSO算法,将自适应粒子集群优化 (APSO) 和鱼优化算法 (WOA) 合并.
- 利用CNN对大米作物的疾病分类.
- 在基准数据集 (Plantvillage) 上进行了实验,重点是特征提取,细分和预处理.
主要成果:
- 混合WOA_APSO算法优化了特征选择,以提高CNN的准确性.
- 实现了高分类准确率97.5%的米疾病.
- 与支持矢量机 (SVM),人工神经网络 (ANN) 和传统的CNN模型相比,表现出更高的性能.
结论:
- 拟议的混合WOA_APSO-CNN模型在自动化病检测方面取得了重大进展.
- 这种方法提高了准确性和效率,解决了现有方法的局限性.
- 这些发现为进一步研究智能农业系统提供了基础.
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