基于密度相互排斥的半监督计数该领域的葡萄
Yanan Li1,2, Yuling Tang1,2, Yifei Liu1,2
1School of Computer Science and Engineering, School of Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, China.
Plant phenomics (Washington, D.C.)
|November 30, 2023
概括
这项研究介绍了CDMENet,这是一个半监督的深度学习方法,用于准确计数葡萄果. 它有效地解决了诸如果密度和遮等挑战,改善了葡萄产量预测.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动葡萄果计数对于预测葡萄产量至关重要.
- 密集的果分布和封闭对深度学习计数算法构成重大挑战.
- 为模型培训收集数据是资源密集的.
研究的目的:
- 提出一个具有成本效益的半监督计数方法,用于在田间条件下的葡萄.
- 利用未标记的数据来提高计数准确度和降低培训成本.
- 增强特征表示,以便更好地区分不同的果密度水平.
主要方法:
- 提出了一个名为CDMENet的半监督计数算法.
- VGG16被用作特征提取的骨干.
- 基于密度相互排斥和密度差损失函数的辅助任务被引入,以利用未标记的数据并增强特征表示.
主要成果:
- CDMENet在田间葡萄果数据集上显示了减少的计数错误.
- 与最先进的方法相比,确定系数 (R2) 提高了6.10%.
- 平均绝对误差和根平均平方误差分别减少了49.36%和54.08%.
结论:
- CDMENet有效地解决了自动葡萄果计数方面的挑战.
- 半监督方法显著提高计数精度和效率.
- 该方法为准确的葡萄产量预测提供了一个有希望的解决方案.
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