CADFFNet:一个双分支的神经网络,用于在空气固化阶段非破坏性检测雪茄叶的水分含量
Zhuoran Xing1, Yaqi Shi2, Yihao Pan1
1College of Tobacco Science, National Tobacco Cultivation and Physiology and Biochemistry Research Center, Key Laboratory for Tobacco Cultivation of Tobacco Industry, Henan Agricultural University, Zhengzhou, China.
Frontiers in plant science
|November 21, 2025
概括
本研究介绍了CADFFNet,这是一个双视图图像网络,用于准确地估计没有破坏的雪茄叶的水分含量 (CLMC). 该方法改进了传统模型,为作物湿度预测提供了一种新的方法.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 雪茄叶的水分含量 (CLMC) 对于治愈谷仓控制至关重要.
- 深度学习 (DL) 和CNN提供非破坏性的CLMC估计方法.
- 单视角成像限制了雪茄叶的全面特征捕捉.
研究的目的:
- 开发用于雪茄叶空气固化过程的双视图数据集.
- 提出一个新的回归框架,CADFFNet,用于使用双视图RGB图像进行非破坏性CLMC估计.
主要方法:
- 构建了空气固化过程的双视图数据集.
- 开发了CADFFNet,一个基于关注重量的双分支特征融合网络.
- 采用了并行ResNet骨干,双效通道注意力 (DECA) 和多尺度卷积特征融合 (MSCFF) 模块.
主要成果:
- 在交叉验证中,CADFFNet实现了0.974的R2和3.80%的MAE.
- 在一个独立的测试集 (R2=0.899,MAE=5.82%) 上显示出强烈的概括性.
- 在R2中表现优于经典的CNN模型,高达0.098.8.
结论:
- CADFFNet提供了一种高效,非破坏性的CLMC检测方法.
- 提供了自动化雪茄空气固化过程的理论基础.
- 为其他作物的水分含量预测提供了一个新的视角.
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