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基于神经网络的研究来识别和分类叶疾病:基于特征的模型和直接成像模型之间的比较分析.

Farida Siddiqi Prity¹, Mirza Raquib², Saydul Akbar Murad³

¹Department of Computer Science and Engineering Netrokona University Netrokona Bangladesh.

Food science & nutrition

|December 22, 2025

概括

这项研究引入了特征分析检测模型 (FADM) 用于早期检测大米叶病,其性能优于直接图像分析. 这种人工智能方法提高了作物健康,并减少了可持续农业的产量损失.

关键词:

人工神经网络的人工神经网络极端学习的机器学习.特性提取算法特性提取算法疾病疾病疾病疾病米米饭米饭米饭米饭.

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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:

病导致大量的产量损失和经济损失.
早期检测对于有效的疾病管理和提高作物产量至关重要.
当前的人工智能方法往往缺乏对特征提取方法的比较分析.

研究的目的:

将特征分析检测模型 (FADM) 与直接图像中心检测模型 (DICDM) 进行比较,用于大米叶病的分类.
评估各种特征提取算法 (FEA),尺寸缩小算法 (DRA) 和特征选择算法 (FSA) 的有效性.
研究极端学习机器 (ELM) 和梯度加权类激活映射 (Grad-CAM) 在病检测中的应用.

主要方法:

实验是在6个类别的3829张大米叶图像的数据集上进行的.
FADM使用了各种FEA,DRA,FSA和ELM.
为了进行比较分析,DICDM在没有FEAs的情况下实施.
用多个指标评估分类性能,并使用Grad-CAM来评估可解释性.

主要成果:

特性分析检测模型 (FADM) 实现了最高的分类性能.
该研究提供了FADM和DICDM之间的全面比较.
通过Grad-CAM可视化证实了模型的可解释性.

结论:

与直接图像分析相比,拟议的FADM在分类大米叶病方面表现优越.
这种人工智能驱动的方法为改善大米作物的健康和可持续性提供了巨大的潜力.
准确的早期检测可以最大限度地减少经济损失,提高农业生产率.