玉米种子预测使用混合定向和双方向长短期记忆模型
Hakan Isik1, Sakir Tasdemir2, Yavuz Selim Taspinar3
1Department of Electric-Electronic Engineering Selcuk University Konya Turkey.
Food science & nutrition
|February 19, 2024
概括
准确的玉米品种分类对于种子纯度和产量至关重要. 研究人员开发了六种模型,ResNet50+BiLSTM混合实现了98.10%的成功,使用深度学习识别了四种玉米类型.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 种子的纯度是影响作物产量的关键因素.
- 准确的玉米品种分类对于保持种子纯度和最大限度地提高农业产量至关重要.
- 目前用于玉米品种识别的方法在准确性和效率方面面临挑战.
研究的目的:
- 开发和评估先进的机器学习模型,用于准确的玉米品种分类.
- 调查深度学习架构的有效性,特别是AlexNet和ResNet50,结合循环神经网络进行基于图像的分类.
- 确定四种不同的玉米品种的高成功分类的最佳模型.
主要方法:
- 一个专门的数据集包括14469张图像,涵盖四种玉米品种 (BT6470,CALIPOS,ES_ARMANDI,HIVA).
- 使用预训练的AlexNet和ResNet50架构进行转移学习,用于图像特征提取.
- 通过将AlexNet和ResNet50与长短期存储器 (LSTM) 和双向长短期存储器 (BiLSTM) 算法集成,创建了混合模型.
主要成果:
- 设计和评估了六种不同的分类模型.
- 当ResNet50架构与双向长短期存储器 (BiLSTM) 算法混合时,它表现出卓越的性能.
- 该ResNet50+BiLSTM模型实现了最高的分类准确度,达到98.10%.
结论:
- 综合卷积神经网络和循环神经网络的混合深度学习模型在玉米品种分类中提供了显著的改进.
- 该ResNet50+BiLSTM模型提供了一个高度有效的解决方案,用于自动和准确识别玉米品种.
- 这种方法有可能加强种子质量控制,并有助于提高农业生产率.
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