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.
Abstract: Microbial infections of crop plants present an ongoing threat to agricultural production. However, in recent years, we have developed a more nuanced understanding of the ecological role of microbes and how they interact with plants. This includes an appreciation of the influence of crop physiology and environmental conditions on the expression of disease symptoms, the importance of non-pathogenic microbes on host plants and pathogens, and the capacity for plants to act as hosts for human pathogens. Alongside this we now have a variety of tools available for the identification and quantification of microbial infections on crops grown under field conditions. This review summarises some of the consequences of microbial infections in crop plants, and discusses how new and established assessment tools can be used to understand these processes. It challenges our current assumptions in yield loss relationships and offers understanding of the potential for more resilient crops.
Abstract: Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm2), whereas image-based methods allow the sampling of entire plots (2 to 30 m2). A higher aerial position allows the rapid measurement of large numbers of plots if the altitude is low (10 to 40 m) and the flight control is sufficiently precise to collect high-resolution images. This paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.