Abstract: Root architecture was determined together with shoot parameters under well watered and drought conditions in the field in three soybean cultivars (A5409RG, Jackson and Prima 2000). Morphology parameters were used to classify the cultivars into different root phenotypes that could be important in conferring drought tolerance traits. A5409RG is a drought-sensitive cultivar with a shallow root phenotype and a root angle of <40°. In contrast, Jackson is a drought-escaping cultivar. It has a deep rooting phenotype with a root angle of >60°. Prima 2000 is an intermediate drought-tolerant cultivar with a root angle of 40°–60°. It has an intermediate root phenotype. Prima 2000 was the best performing cultivar under drought stress, having the greatest shoot biomass and grain yield under limited water availability. It had abundant root nodules even under drought conditions. A positive correlation was observed between nodule size, above-ground biomass and seed yield under well-watered and drought conditions. These findings demonstrate that root system phenotyping using markers that are easy-to-apply under field conditions can be used to determine genotypic differences in drought tolerance in soybean. The strong association between root and nodule parameters and whole plant productivity demonstrates the potential application of simple root phenotypic markers in screening for drought tolerance in soybean.
Abstract: The improvements in crop production needed to meet the increasing food demand in the 21st Century will rely on improved crop management and better crop varieties. In the last decade our ability to use genetics and genomics in crop science has been revolutionised, but these advances have not been matched by our ability to phenotype crops. As rapid and effective phenotyping is the basis of any large genetic study, there is an urgent need to utilise the recent advances in crop scale imaging to develop robust high-throughput phenotyping. This review discusses the use and adaptation of infra-red thermography (IRT) on crops as a phenotyping resource for both biotic and abiotic stresses. In particular, it addresses the complications caused by external factors such as environmental fluctuations and the difficulties caused by mixed pixels in the interpretation of IRT data and their effects on sensitivity and reproducibility for the detection of different stresses. Further, it highlights the improvements needed in using this technique for quantification of genetic variation and its integration with multiple sensor technology for development as a high-throughput and precise phenotyping approach for future crop breeding.
Abstract: The consequences of changes in spatial resolution for application of thermal imagery in plant phenotyping in the field are discussed. Where image pixels are significantly smaller than the objects of interest (e.g., leaves), accurate estimates of leaf temperature are possible, but when pixels reach the same scale or larger than the objects of interest, the observed temperatures become significantly biased by the background temperature as a result of the presence of mixed pixels. Approaches to the estimation of the true leaf temperature that apply both at the whole-pixel level and at the sub-pixel level are reviewed and discussed.
Abstract: The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.
Abstract: GPS guidance of farm machinery has been increasingly adopted by farmers because of the perceived gains in efficiency that it provides. In the southeastern USA one of the reasons farmers adopt GPS guidance, and specifically automated steering (auto-steer), is that it can theoretically result in large yield gains when used to plant and invert peanuts—one of the region’s most important crops. The goal of our study was to quantify the yield benefit of using real time kinematic (RTK)-based auto-steer to plant and invert peanuts under a variety of terrain conditions. Yield benefits result from reduced digging losses. The study was conducted for two consecutive years (2010 and 2011) on a private farm in Georgia, USA. When all data are grouped together, auto-steer outperformed conventional by 579 kg/ha in 2010 and 451 kg/ha in 2011. We also evaluated the performance of auto-steer under different curvature conditions using low, medium, and high curvature rows. The results showed that auto-steer outperformed conventional under all curvature by a minimum of 338 kg/ha. Finally, we evaluated passive implement guidance in combination with auto-steer and found that it holds tremendous potential for further reducing digging losses. In many cases, auto-steer will pay for itself within a year.
Abstract: Phenotyping in field experiments is challenging due to interactions between plants and effects from biotic and abiotic factors which increase complexity in plant development. In such environments, visual or destructive measurements are considered the limiting factor and novel approaches are necessary. Remote multispectral imaging is a powerful method that has shown significant potential to estimate crop physiology. However, precise measurements of phenotypic differences between crop varieties in field experiments require exclusion of the disturbances caused by wind and varying sunlight. A mobile and closed multispectral imaging system was developed to study canopies in field experiments. This system shuts out wind and sunlight to ensure the highest possible precision and accuracy. Multispectral images were acquired in an experiment with four different wheat varieties, two different nitrogen levels, replicated on two different soil types at four different dates from 15 May (BBCH 13) to 18 June (BBCH 41 to 57). The images were analyzed and derived vegetation coverage and Normalized Difference Vegetation index (NDVI) were used to assess varietal differences. The results showed potentials for differentiating between the varieties using both vegetation coverage and NDVI, especially at the early growth stages. The perspectives of high-precision and high-throughput imaging for field phenotyping are discussed including the potentials of measuring varietal differences via spectral imaging in comparison to other simpler technologies such as spectral reflectance and RGB imaging.