在植物叶子中诱导的疾病的多重分类使用时间顺序的弗拉明戈搜索优化与转移学习
Malathi Chilakalapudi1, Sheela Jayachandran1
1SCOPE, VIT-AP University, Amaravathi, Andhra Pradesh, India.
PeerJ. Computer science
|April 25, 2024
概括
这项研究引入了一种使用计算机视觉早期检测植物疾病的新方法. 该方法将转移学习 (TL) 与一种新的算法相结合,在从叶子图像中识别疾病方面实现了高准确性.
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:
- 植物病严重影响作物产量和质量,需要有效的检测方法.
- 手动检测疾病是劳动密集型和耗时的.
- 早期发现疾病对于最大限度地减少经济损失和提高农业生产率至关重要.
研究的目的:
- 开发一种自动化系统,使用叶子图像对植物疾病进行多重分类.
- 通过先进的算法和转移学习,提高植物疾病检测的准确性和效率.
主要方法:
- 图像预处理使用适应性无异型扩散来降低噪音.
- 使用U-Net++训练使用移动大猩猩雷莫拉算法 (MGRA) 的植物叶片细分.
- 位置和颜色增强的应用,以减少维度.
- 特性提取和多层次分类 (植物类型和疾病) 使用卷积神经网络 (CNN) 与转移学习 (TL) 和时间 Flamingo 搜索算法 (CFSA).
主要成果:
- 拟议的基于CFSA-TL的CNN与LeNet实现了高性能指标.
- 在检测植物疾病方面,获得了95.7%的准确性,96.5%的灵敏度和94.7%的特异性.
- 证明了综合方法的有效性,用于早期和准确的疾病识别.
结论:
- 开发的模型为早期检测植物叶病提供了强大而高效的解决方案.
- 这种基于计算机视觉的方法可以显著帮助农民管理作物健康并优化产量.
- CFSA与TL和CNN的整合为农业疾病诊断提供了一个有希望的方向.
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