Field phenotyping of grapevine growth using dense stereo reconstruction

Background

The demand for high-throughput and objective phenotyping in plant research has been increasing during the last years due to large experimental sites. Sensor-based, non-invasive and automated processes are needed to overcome the phenotypic bottleneck, which limits data volumes on account of manual evaluations. A major challenge for sensor-based phenotyping in vineyards is the distinction between the grapevine in the foreground and the field in the background – this is especially the case for red-green-blue (RGB) images, where similar color distributions occur both in the foreground plant and in the field and background plants. However, RGB cameras are a suitable tool in the field because they provide high-resolution data at fast acquisition rates with robustness to outdoor illumination.

Results

This study presents a method to segment the phenotypic classes ‘leaf’, ‘stem’, ‘grape’ and ‘background’ in RGB images that were taken with a standard consumer camera in vineyards. Background subtraction is achieved by taking two images of each plant for depth reconstruction. The color information is furthermore used to distinguish the leaves from stem and grapes in the foreground. The presented approach allows for objective computation of phenotypic traits like 3D leaf surface areas and fruit-to-leaf ratios. The method has been successfully applied to objective assessment of growth habits of new breeding lines. To this end, leaf areas of two breeding lines were monitored and compared with traditional cultivars. A statistical analysis of the method shows a significant (p <0.001) determination coefficient R ²= 0.93 and root-mean-square error of 3.0%.

Conclusions

The presented approach allows for non-invasive, fast and objective assessment of plant growth. The main contributions of this study are 1) the robust segmentation of RGB images taken from a standard consumer camera directly in the field, 2) in particular, the robust background subtraction via reconstruction of dense depth maps, and 3) phenotypic applications to monitoring of plant growth and computation of fruit-to-leaf ratios in 3D. This advance provides a promising tool for high-throughput, automated image acquisition, e.g., for field robots.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0560-x) contains supplementary material, which is available to authorized users.

[Esophago-thoracic fistula after pneumonectomy and long-term main bronchus stump fistula]

A woman patient admitted for treatment, who had reached the age of 65 years, had a previous history of an open pulmonary tuberculosis in 1946 that had been treated at that time by means of collapsotherapy and phrenico-exeresis. Pleuropneumonectomy was performed in 1976 because of a residual cavity of a thoracic empyema. Since 1978 the patient suffered from a fistula of the bronchus treated by postural therapy without achieving a cure. In 1989 a fistula formed between oesophagus and pneumonectomy cavity. Clinically this was associated with an increasing reduction of performance and a suddenly ineffective postural drainage, resulting in triphasic and eventually fatal aspiration. Histology revealed a suppurative inflammation in the fistular channel and a slight superficial Candida colonisation of the pneumonectomy cavity, of the fistular channel and of adjacent mucous glands of the oesophagus. Formation of the fistula was probably due to a small traction diverticulum followed by perforation because of obstructed oesophageal passage due to scarified distortions.