使用转移窗口变压器神经网络和基于 (YOLO) v9 路径聚合网络的本地化进行麦作物分类和细分
Javaria Amin1, Rida Zahra2, Alena Maryum3
1Department of Computer Science, Rawalpindi Women University, Rawalpindi, Pakistan.
Frontiers in plant science
|October 9, 2025
概括
准确的石榴产量估计对于粮食安全至关重要. 这项研究引入了先进的AI模型,用于精确的作物分类,本地化和细分,优于现有方法.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 全球人口增长需要加强粮食安全,是发展中国家的主要主食作物.
- 准确的果产量预测对于提高生产率至关重要.
- 作物分析的自动化方法显示出有希望的结果,但由于的外观变化而面临挑战.
研究的目的:
- 开发和评估用于作物分类,本地化和细分的先进人工智能模型.
- 为了解决由颜色,形状,照明和噪声的变化引起的图像分析的挑战.
- 通过精确的作物头部分析,提高产量估计的准确性.
主要方法:
- 一个转移窗口变压器 (SWT) 网络被设计用于类别.
- 采用YOLOv9-c模型用于土地区的本地化.
- 一个基于变压器的SegNet模型,利用一个微调的SegFormer-B0,被开发用于精确的小麦粉细分.
主要成果:
- 与现有出版作品相比,拟议的模型表现出优越的性能.
- 综合方法实现了作物的准确分类,本地化和细分.
- 开发的方法有效地处理来自照明和噪音变化的图像复杂性.
结论:
- 该研究提出了一个强大的AI驱动的方法论,用于作物分析.
- 先进的模型为提高谷产量估计准确度做出了重大贡献.
- 这项研究支持提高农业生产率和确保全球粮食安全的努力.
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