基于DSAF-ResNet的冬季朱树的成熟度分类模型
Yufei Song1,2,3, Aoran Liu4,5, Xi Meng3
1College of Horticulture, Hebei Agricultural University, Baoding, China.
NPJ science of food
|August 25, 2025
概括
准确的冬季树成熟度分类是使用一种新的双流注意力融合残留网络 (DSAF-ResNet) 来实现的. 这种方法结合了高光谱和纹理数据,以改善农业的智能收获和质量控制.
科学领域:
- 农业工程
- 计算机视觉
- 机器学习
背景情况:
- 准确的,非破坏性的冬季朱成熟度分类对于优化收获时间和确保水果质量至关重要.
- 目前的方法往往缺乏区分细微成熟阶段所需的精度,影响收获后的过程.
研究的目的:
- 开发和评估一种新的深度学习模型,以准确,非破坏性地对冬季朱成熟度进行分类.
- 为了提高成熟度评估,研究融合高光谱和纹理特征的有效性.
主要方法:
- 一个双流注意力融合的残余网络 (DSAF-ResNet) 被提议,整合了高光谱成像和灰度共发生矩阵 (GLCM) 纹理特征.
- 该网络在双流架构中结合了RepVGGBlock和SimAM注意力机制.
- 通过测试准确度,精度和回忆度来验证模型性能.
主要成果:
- 与单一模式输入相比,融合多模式方法显著提高了分类性能.
- DSAF-ResNet实现了高测试准确度 (97.24%),精度 (97.31%) 和回忆 (97.24%).
- 废弃研究证实了个别网络组件和融合策略的有效性.
结论:
- DSAF-ResNet提供了一个有效和可扩展的框架,用于非破坏性的果实成熟度分类.
- 这种方法增强了智能农业实践,并通过可靠的成熟度评估来支持精准农业.
- 该模型表现出优异的概括性和稳定性,即使数据集不平衡.
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