Sadeghi-Tehran Pouria, Virlet Nicolas, Sabermanesh Kasra, Hawkesford Malcolm J
Plant Science Department, Rothamsted Research, Harpenden, AL5 2JQ UK.
Plant Methods. 2017 Nov 21;13:103. doi: 10.1186/s13007-017-0253-8. eCollection 2017.
Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments.
In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy.
The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.
在表型分析中，从数字图像的背景中准确分割出植被既是一项基础任务，也是一项具有挑战性的任务。传统方法在均匀环境中的性能令人满意，然而，当应用于在动态田间环境中获取的图像时，性能会下降。
本文提出了一种多特征学习方法，用于量化室外田间条件下的植被生长。将所介绍的技术与数字图像上的最新技术和其他学习方法进行了比较。所有方法都在不同环境条件下根据以下标准进行了比较和评估：（1）与地面真值图像比较；（2）随一天中环境光照变化的变化情况；（3）与人工测量比较；（4）对小麦冠层整个生命周期的性能估计。
所描述的方法能够应对田间条件下面临的环境挑战，具有高度的适应性，且无需为每张数字图像调整阈值。所提出的方法也是处理田间获取的作物生长过程中表型信息时间序列的理想候选方法。此外，所介绍的方法具有一个优势，即它不仅限于生长测量，还可应用于其他应用，如识别杂草、疾病、胁迫等。
