在粮食作物中检测和分类叶病,具有高效的特征尺寸性减少
Khasim Syed1, Shaik Salma Asiya Begum2, Anitha Rani Palakayala1
1School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
PloS one
|August 1, 2025
概括
本研究介绍了一种利用CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) 模型进行叶子图像分类的高效标记特征尺寸缩小方法. 该模型在疾病识别中达到99.37%的准确性,增强了精准农业.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业科学 农业科学
背景情况:
- 计算机视觉依赖于特征提取来进行图像分类.
- 降低维度对于深度学习模型中的计算效率至关重要.
- 叶子图像分析面临着高维度的挑战,影响疾病识别的准确性.
研究的目的:
- 提出一种新的计算机视觉系统,集成卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 网络.
- 通过有效的特征缩小来解决叶子图像数据的高维度.
- 能够及早准确地识别植物疾病,以改善作物管理.
主要方法:
- 使用CNN进行特征提取.
- 使用BiLSTM网络进行时间依赖建模.
- 标签信息的整合作为歧视性特征学习的约束.
- 使用拟议的ELFDR-LDC-CNN-BiLSTM模型来减少提取特征的尺寸.
主要成果:
- 在胡和玉米叶图像数据集上实现了99.37%的分类准确度.
- 与现有的尺寸缩小技术相比,表现出更高的性能.
- 成功地减少了特征维度,同时提高了分类的有效性.
结论:
- 拟议的ELFDR-LDC-CNN-BiLSTM模型为自动检测叶病提供了一个具有成本效益的解决方案.
- 该系统可以集成到精准农业中,以加强作物监测和提高产量.
- 这种方法通过早期和准确的疾病识别来促进可持续的农业实践.
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