一个基于深度学习的多类分类器,用于对叶病的严重程度进行微调
Shweta Lamba1, Vinay Kukreja2, Junaid Rashid3
1Chandigarh Engineering College, CGC Landran, Mohali, India.
Frontiers in plant science
|September 21, 2023
概括
这项研究引入了一种混合深度学习模型,用于准确检测和评估叶病的严重程度. 该模型在识别细菌病菌,爆破和叶子污垢方面达到98.43%的准确性,改善了作物产量预测.
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:
- 叶病严重影响作物产量和质量.
- 准确的检测和严重程度评估对于有效的疾病管理至关重要.
研究的目的:
- 开发和评估一种混合深度学习模型,用于识别叶病及其严重程度.
- 提高在米作物中疾病诊断的准确性和效率.
主要方法:
- 一个由4068张叶图像组成的数据集被预处理并使用生成对抗网络 (GAN) 进行增强.
- 一个混合模型,结合卷积神经网络 (CNN) 和支持矢量机器 (SVM) 被用于分类.
- 细分方法用于疾病严重程度的计算,严重程度水平由领域专家定义.
主要成果:
- 混合CNN-SVM模型在预测米疾病类型和强度方面实现了98.43%的准确率.
- 该模型有效地分类了三种主要疾病:细菌疹,疹和叶子疹.
- 与独立的CNN和SVM模型相比,拟议的模型表现出优越的性能.
结论:
- 开发的混合深度学习方法可靠且有效地识别和评估关键的叶疾病的严重程度.
- 这种方法比现有的作物监测分类模型提供了显著的进步.
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