CSCA-YOLOv8:一个轻量级的网络模型,用于评估豆的抗干旱能力
Dongshan Jiang1,2, Jinyang Liu2, Haomiao Zhang1,2
1Department of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.
PloS one
|July 31, 2025
概括
一个新的轻量级人工智能模型,CSCA-YOLOv8,利用叶绿素光图像有效地识别了豆的干旱抵抗力. 这种模型提高了准确性,同时减少了尺寸,有助于对耐旱品种的生殖质选择.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物生理学 植物生理学
背景情况:
- 干旱严重影响中国的豆生产,需要耐干旱品种.
- 叶绿素光成像提供了一种非破坏性的方法来评估植物压力.
- 开发高效的计算模型对于大规模的生殖质查至关重要.
研究的目的:
- 提出一种轻量级的深度学习模型,用于识别豆对干旱的抵抗力.
- 为了提高干旱抵抗性识别在豆的效率和准确性.
- 为了方便在嵌入式边缘设备上部署抗干旱识别模型.
主要方法:
- 开发了一个基于YOLOv8.8的轻量级豆干旱抵抗识别网络 (CSCA-YOLOv8).
- 集成的StarNet作为骨干,C2f_Star模块,以及一个上下文注意力机制 (CAA) 模块.
- 使用CGBD模块来改进小目标特征提取和SIoU损失以实现更快的融合.
- 创建了Mungbean干旱数据集 (MDD),其中包含4808张叶绿素光图像.
主要成果:
- 与YOLOv8s基线相比,CSCA-YOLOv8模型的准确性提高了2.52%.
- 模型参数减少了24%,浮点运算减少了35%.
- 该模型在MDD数据集上表现出色的性能和概括性.
结论:
- 拟议的CSCA-YOLOv8模型有效地识别了豆的抗干旱性.
- 它的轻量级设计使其适合在资源有限的边缘设备上部署.
- 这项技术具有很大的潜力,可以促进豆的繁殖和生殖质选择.
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