Phenoliner 2.0: RGB and near-infrared (NIR) image acquisition for an efficient phenotyping in grapevine research
In grapevine research, phenotyping needs to be done for different traits such as abiotic and biotic stress. This phenotypic data acquisition is very time-consuming and subjective due to the limitation of manual visual estimation. Sensor-based approaches showed an improvement in objectivity and throughput in the past. For example, the 'Phenoliner' a phenotyping platform, based on a modified grape harvester, is equipped with two different sensor systems to acquire images in the field. It has so far been used in grapevine research for different research questions to test and apply different sensor systems. However, the driving speed for data acquisition has been limited to 0.5 - 1 km/ h due to capacity of image acquisition frequency and storage. Therefore, a faster automatic data acquisition with high objectivity and precision is desirable to increase the phenotyping efficiency. To this aim, in the present study a prism-based simultaneous multispectral camera system was installed in the tunnel of the 'Phenoliner' with an artificial broadband light source for image acquisition. It consists of a visible color channel from 400 to 670 nm, a near infrared (NIR) channel from 700 to 800 nm, and a second NIR channel from 820 to 1,000 nm. Compared to the existing camera setup, image recording could be improved to at least 10 images per second and a driving speed of at least 5 km/h. Each image is geo-referenced using a real-time-kinematic (RTK)-GPS system. The setup of the sensor system was tested on seven cultivars (Riesling, Pinot Noir, Chardonnay, Dornfelder, Dapako, Pinot Gris, and Phoenix) with and without symptoms of biotic stress in the vineyards of Geilweilerhof, Germany. Image analysis aims to segment images into four categories: background, leaves, grapes and wood to further detect the biotic stress status in these categories. Therefore, images have been annotated accordingly and first results will be shown.