However, high quality camera systems often still exceed the payload of available drones. Yet, there is little progress on methods and platforms that operate from the air although currently drones are becoming increasingly popular for aerial photography. For field-based methods, progress has been made mostly using camera-based approaches that are mounted on ground-based vehicles like tractors (e.g. , for a review see ), the development of field-based phenotyping approaches has lagged. Whereas laboratory-based phenotyping platforms that monitor the performance of single plants of model species have advanced greatly in recent years (e.g. Remaining bottlenecks are: sufficient ground resolution, particularly for thermal imaging, as well as a deeper understanding of the relatedness of remotely sensed data and basic crop characteristics.įield-based high-throughput phenotyping methods are urgently needed by plant breeding research. Important developments are: the ability to monitor throughout the season, robust image segmentation and the identification of individual plots in images from different sensor types at different dates. We made important steps towards automated processing of remotely sensed data, and demonstrated the value of several procedural steps, facilitating the application in plant genetics and breeding. However, for thermography, more than two rows improve the precision. Based on repeatability values, we consider two row plots as best choice to balance between precision and available field space. Thermal images revealed information about the combination of radiation interception, early vigour, biomass, plant height and LAI. This could be explained, at least partially, by methodical differences between the used devices and effects imposed by the population structure. Unexpectedly, NDVI Plant correlated negatively with chlorophyll meter measurements. However, it related well to the vigour rating, leaf area index (LAI) and leaf biomass around flowering and to very late senescence rating. NDVI Plant was less well related to ground truth data.
Remotely sensed CC correlated well with plant density, early vigour, leaf size, and radiation interception. NDVI images were better suited than RGB images to segment plants from soil background leading to two separate traits: the canopy cover (CC) and its NDVI value (NDVI Plant). Ground control points were used to co-register the images and to overlay them with a plot map. Using the platform, red, green and blue colour space (RGB), normalized difference vegetation index (NDVI) and thermal images were acquired throughout the growing season and compared with traits measured on the ground. Additionally there was no restriction in sensor weight. The system has the advantage of a low altitude and cruising speed compared to many drones or airplanes, thus enhancing image resolution while reducing blurring effects. We present a novel method for repeated remote phenotyping of maize genotypes using the Zeppelin NT aircraft as an experimental sensor platform. Field-based high throughput phenotyping is a bottleneck for crop breeding research.
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