GCNet:一种深度学习框架,用于增强葡萄分段和产量估计,包括封闭葡萄检测和室内实验的校正因子.
Rubi Quiñones1, Syeda Mariah Banu1, Eren Gultepe1
1Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA.
Journal of imaging
|February 25, 2025
概括
葡萄计数网络 (GCNet) 通过使用深度学习和校正因子准确计数封闭的葡萄,提高了葡萄产量估计. 这种新的框架增强了农业成像分析,以更好地进行葡萄分析.
科学领域:
- 农业成像技术 农业成像技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 用于葡萄产量估计的对象细分算法受限于它们无法计算封闭的葡萄.
- 由于集群紧性或树冠干扰,隐藏的葡萄导致产量预测不准确.
研究的目的:
- 开发一种新的框架,葡萄计数网络 (GCNet),用于准确的葡萄集群细分和产量估计.
- 为了应对室内农业成像中计数封闭葡萄的挑战.
主要方法:
- 提出了葡萄计数网络 (GCNet),将深度学习与阻塞调整的校正因子集成在一起.
- 开发了GrapeSet,这是一个新的数据集,包含室内葡萄群图像和地面真相数据.
- 利用先进的对象细分技术来改进葡萄分析.
主要成果:
- 与以前的方法相比,GCNet实现了0.96的R2并将平均绝对误差 (MAE) 降低了10%.
- 在具有挑战性的条件下,如树叶和集群的紧性,证明了增强的细分精度.
- 成功解决了仅计数可见葡萄的限制.
结论:
- 在农业室内成像分析中,GCNet为葡萄产量估计设定了新的标准.
- 该框架为葡萄分析提供了准确的对象细分,有助于收割者预测早期产量.
- 鼓励未来对葡萄特征分析进行产量估计的研究.
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