使用深度二值分类器估计植物中心

Yuhao Chen, Javier Ribera, E. Delp
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引用次数: 4

摘要

表型是估计植物的物理和化学特性的过程。传统的表型分析是劳动密集型和耗时的。通过使用无人驾驶飞行器(UAV)收集航空图像并使用现代图像分析技术对其进行分析,可以更快地获得这些测量结果。我们提出了一种通过将每个像素分类为植物中心或非植物中心来估计植物中心的方法。然后我们将每个簇的中心标记为植物位置。我们在两个数据集上研究了我们的方法的性能。我们在一个由早期植物组成的数据集上实现了84%的精度和90%的召回率,在另一个由后期植物组成的数据集上实现了62%的精度和77%的召回率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Plant Centers Using A Deep Binary Classifier
Phenotyping is the process of estimating the physical and chemical properties of a plant. Traditional phenotyping is labor intensive and time consuming. These measurements can be obtained faster by collecting aerial images with an Unmanned Aerial Vehicle (UAV) and analyzing them using modern image analysis technologies. We propose a method to estimate plant centers by classifying each pixel as a plant center or not a plant center. We then label the center of each cluster as the plant location. We studied the performance of our method on two datasets. We achieved 84% precision and 90% recall on one dataset consisting of early stage plants and 62% precision and 77% recall on another dataset consisting of later stage plants.
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