论文标题
使用元数据转移农业中的光度表型
Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata
论文作者
论文摘要
在田间条件下的光度植物表型（例如色调，光泽，色度）的估计对于决定预期的产量质量，成熟度和进一步繁殖的需求很重要。由于在照明条件，阴影和传感器属性中的差异较大，因此很难从图像中估算这些图像。我们将图像和元数据结合在一起，以捕获嵌入到网络中的捕获条件，从而实现更准确的估计和不同条件之间的传递。与最先进的Deep CNN和人类专家相比，嵌入元数据可改善对番茄色调和色度的估计。
Estimation of photometric plant phenotypes (e.g., hue, shine, chroma) in field conditions is important for decisions on the expected yield quality, fruit ripeness, and need for further breeding. Estimating these from images is difficult due to large variances in lighting conditions, shadows, and sensor properties. We combine the image and metadata regarding capturing conditions embedded into a network, enabling more accurate estimation and transfer between different conditions. Compared to a state-of-the-art deep CNN and a human expert, metadata embedding improves the estimation of the tomato's hue and chroma.
