利用深度学习模型进行果实疾病的早期检测和分类:朝着可持续农业和提高食品质量的方向
Ibrahim Alrashdi1, Marwa Sharawi2, Ahmed M Ali3
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, 2014, Sakaka, Saudi Arabia.
Scientific reports
|February 10, 2026
概括
这项研究引入了深度学习模型,用于早期检测水果疾病,实现高精度. 建议使用EfficientNetB3模型,因为它在分类各种植物疾病方面具有卓越的性能.
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:
- 果实疾病对全球粮食生产力和质量产生重大影响.
- 手动视觉分类用于检测水果疾病具有挑战性,并且往往不准确.
- 深度学习为准确和高效的疾病分类提供先进的解决方案.
研究的目的:
- 开发和评估深度学习模型,用于早期水果疾病检测和分类.
- 为了比较五种不同的深度学习架构的性能.
- 确定改善农业实践的最有效模式.
主要方法:
- 应用了五种深度学习模型:卷积神经网络 (CNN),DenseNet121,EfficientNetB3,Xception,以及ResNet50. 这些都是深度学习模型.
- 利用图像预处理和数据增强来进行增强的图像分析.
- 训练模型检测子,葡萄,果,瓜,果和香植物的六种水果疾病.
主要成果:
- 实现了很高的检测准确度,EfficientNetB3在葡萄疾病中达到99.14%,在果疾病中达到99.33%.
- 证明了结合图像预处理和数据增强的模型产生了更高的性能.
- 效率NetB3,ResNet50和CNN模型在不同水果类型中显示出强的结果.
结论:
- 深度学习模型,特别是EfficientNetB3,对于早期和准确的水果疾病检测非常有效.
- 图像预处理和数据增强对于最大限度地提高模型性能至关重要.
- 推的EfficientNetB3模型可以显著帮助农民在疾病管理,提高食品质量和生产率.
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