预测和可视化果颜色转换使用深度面具引导生成网络
Zehan Bao1, Weifu Li1,2, Jun Chen1,2
1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Plant phenomics (Washington, D.C.)
|June 9, 2023
概括
预测皮的颜色转换现在可以通过新的AI框架来实现. 这项技术通过准确预测水果发育阶段,有助于作物管理和收获计划.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 果皮肤的颜色是水果成熟和发育的关键指标.
- 准确监测和预测皮肤颜色变化对于优化作物管理和收获时间至关重要.
研究的目的:
- 开发和验证一个高精度的工作流程,用于预测和可视化皮颜色转换.
- 创建一个能够预测未来各种时间点皮肤颜色的多功能模型.
主要方法:
- 一个新的框架,将视觉突出与深度学习相结合,包括细分,生成和丢失网络.
- 在单个模型中融合图像特征和时间信息,以预测随时间变化的颜色变化.
- 在颜色转换过程中开发了一套数据集,其中包括来自107个腹子的7,535张图像.
主要成果:
- 语义细分网络实现了0.9694.4的工会平均交叉点的平均分数.
- 生成网络显示出高图像质量和相似性,峰值信号噪声比为30.01.
- 该模型的预测与人类的感知一致,并通过定量指标得到验证.
结论:
- 拟议的AI框架准确地预测和可视化皮颜色转换.
- 该模型的效率和准确性适用于现实应用,包括移动应用.
- 该方法适用于预测其他水果作物的颜色变化.
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