使用强大的编码-解码级联深度学习模型进行植物叶子感染点细分
David Femi1, Manapakkam Anandan Mukunthan1
1Research Scholar, Professor Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
Network (Bristol, England)
|November 30, 2023
概括
一个新的深度编码解码级联网络 (DEDCNet) 精确地细分了病变的叶子斑点,改善了植物疾病的诊断. 这种先进的模型通过准确识别和分类各种叶子感染来提高农业产量.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的叶病诊断对于农业生产率和降低成本至关重要.
- 病变的叶子斑点的不准确细分可能导致植物疾病的错误分类.
- 疾病特征和尺寸的重叠对精确的细分构成了挑战.
研究的目的:
- 提出一种新的深度编码解码级联网络 (DEDCNet),用于精确的叶子图像分割.
- 为了准确地细分病变的叶子斑点,并区分相似的植物疾病.
- 提高叶病分类和诊断的整体准确性.
主要方法:
- 开发了一个DEDCNet模型,包括一个感染点识别网络 (ISRN) 和一个感染点细分网络 (ISSN).
- ISRN将级联卷积神经网络 (CNN) 与特征金字塔聚合集成,用于感染点的识别.
- 为了实现精确的细分,ISSN采用了一个编码器-解码器架构,具有多尺度扩展卷积.
- 使用预先学习的CNN来提取纹理特征,以及用于疾病分类的支持矢量机 (SVM).
主要成果:
- 在高精度,回忆和F-score的贝特尔叶图像数据集上实现了94.89%的准确性.
- 在贝特利叶数据集中,低低的低细分率 (6.2%) 和过度细分率 (2.8%) 已被证明.
- 在PlantVillage数据集上达到96.5%的准确性,优于现有模型.
- 在不到0.1秒的时间内,在各自的数据集上实现了0.9822和0.9834的子系数.
结论:
- DEDCNet模型显著提高了叶病细分和分类准确度.
- 与现有的植物疾病分析模型相比,拟议的方法提供了更高的效率.
- 准确的细分对于可靠的疾病诊断至关重要,有助于改善农业成果.
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