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在孟加拉国优化作物疾病识别:深度学习和SVM混合模型用于大米,土豆和玉米

Shohag Barman¹, Fahmid Al Farid², Jaohar Raihan³

¹Department of Computer Science & Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Pirojpur 8500, Bangladesh.

Journal of imaging

|August 28, 2024

概括

一个新的混合深度学习模型准确地识别了孟加拉国的作物疾病,提高了农业产量和粮食安全. 这种先进的系统结合了EfficientNetB0和支持矢量机器 (SVM) 来精确检测疾病.

科学领域:

农业科学农业科学
计算机科学计算机科学
机器学习机器学习

背景情况:

农作物疾病严重威胁孟加拉国农业部门和粮食安全.
及时准确地识别疾病对于可持续的粮食生产至关重要.

研究的目的:

开发一种混合深度学习模型,用于在孟加拉国识别三个特定的作物疾病.
提高精准农业中的计算效率和准确性.

主要方法:

开发了一个混合模型,集成了EfficientNetB0用于特征提取和支持向量机 (SVM) 进行分类.
该模型经过训练并对土豆晚期腐烂,大米棕色斑点和玉米常见生的数据进行了测试.

主要成果:

拟议的混合模型实现了97.29%的高精度.
对比分析显示,与CNN,VGG16,ResNet50,Xception,Mobilenet V2,Autoencoders,Inception v3和EfficientNetB0.0等单个模型相比,它们的性能优越.

结论:

混合EfficientNetB0-SVM模型在作物疾病识别方面表现出卓越的性能.
这种方法为加强准确农业和确保孟加拉国的粮食安全提供了有希望的解决方案.

关键词:

有效网B0 有效网B0 这是分类分类的分类.卷积神经网络 (CNN) 是一种神经网络.功能提取特性提取支持向量机器 (SVMs) 的使用.

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