Abstract
Apple trees being deciduous trees, shed leaves each year which is preceded by the change in color of leaves from green to yellow (also known as senescence) during the fall season. The rate and timing of color change are affected by the number of factors including nitrogen (N) deficiencies. The green color of leaves is highly dependent on the chlorophyll content, which in turn depends on the nitrogen concentration in the leaves. The assessment of the leaf color can give vital information on the nutrient status of the tree. The use of a machine vision based system to capture and quantify these timings and changes in leaf color can be a great tool for that purpose. \par This study is based on data collected during the fall of 2021 and 2023 at a commercial orchard using a ground-based stereo-vision sensor for five weeks. The point cloud obtained from the sensor was segmented to get just the tree in the foreground. The study involved the segmentation of the trees in a natural background using point cloud data and quantification of the color using a custom-defined metric, \textit{yellowness index}, varying from $-1$ to $+1$ ($-1$ being completely green and $+1$ being completely yellow), which gives the proportion of yellow leaves on a tree. The performance of K-means based algorithm and gradient boosting algorithm were compared for \textit{yellowness index} calculation. The segmentation method proposed in the study was able to estimate the \textit{yellowness index} on the trees with $R^2 = 0.72$. The results showed that the metric was able to capture the gradual color transition from green to yellow over the study duration. It was also observed that the trees with lower nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. The onset of color transition during both years aligned with the $29^{th}$ week post-full bloom.
Abstract (translated)
苹果树是一种落叶树，每年在秋季都会落叶，落叶之前，树叶的颜色从绿色变为黄色（也称为衰老） 。树叶颜色变化的速度和时间受多种因素影响，包括氮（N）不足。树叶绿色程度高度依赖于叶绿素的含量，而叶绿素的含量又取决于叶片中的氮浓度。评估树叶颜色可以提供关于树木营养状态的重要信息。利用基于机器视觉的系统来捕获和量化这些时间和树叶颜色变化可以成为达到这一目的的好工具。 这项研究基于2021年和2023年在商业或园艺场收集的数据，采用基于地面立体视觉传感器五周的时间。从传感器获得的点云数据对树木进行了分割，只获得了前景树。研究涉及使用点云数据分割自然背景中的树木，并使用自定义定义的指标（黄色指数）对颜色进行量化，该指标的取值范围从-1到+1（-1表示完全绿色，+1表示完全黄色），给出树木上黄色叶子的比例。比较了K-means基于算法和梯度提升算法在黄色指数计算方面的性能。研究提出的分割方法在R2=0.72的树木上估计了黄色指数。结果显示，该指标能够捕捉研究期间树叶颜色从绿色到黄色的逐渐转变。此外，还观察到氮含量较低的树木颜色转变到黄色的时间比氮含量较高的树木早。两年间树叶颜色转变的发病时间与第29周花全放后一致。
URL
https://arxiv.org/abs/2404.14653
