The scientific areas of plant genomics and phenomics are capable of improving plant productivity, yet they are limited by the manual labor that is currently required to perform in-field measurement, and a lack of technology for measuring the physical performance of crops growing in the field. A variety of sensor technology has the potential to efficiently measure plant characteristics that are related to production. Recent advances have also shown that autonomous airborne and manually driven ground-based sensor platforms provide practical mechanisms for deploying the sensors in the field. This paper advances the state-of-the-art by developing and rigorously testing an efficient system for high throughput in-field agricultural row-crop phenotyping. The system comprises an autonomous unmanned ground-vehicle robot for data acquisition and an efficient data post-processing framework to provide phenotype information over large-scale real-world plant-science trials. Experiments were performed at three trial locations at two different times of year, resulting in a total traversal of 43.8 km to scan 7.24 hectares and 2423 plots (including repeated scans). The height and canopy closure data were found to be highly repeatable (r 2 = 1.00 N = 280, r 2 = 0.99 N = 280, respectively) and accurate with respect to manually gathered field data (r 2 = 0.95 N = 470, r 2 = 0.91 N = 361, respectively), yet more objective and less-reliant on human skill and experience. The system was found to be a more labor-efficient mechanism for gathering data, which compares favorably to current standard manual practices. K E Y W O R D Sagriculture, hyperspectral and lidar sensing, plant phenomics, row-crop phenotyping, terrestrial robotics INTRODUCTIONPredicted global population increases are expected to cause a doubling in food demand by 2050, while at the same time the ability to grow more food is threatened by problems of water scarcity, soil fertility, and climate change. 12 Significant increases in food production are required, which will necessitate greater productivity in terms of yield per hectare and efficient use of natural resources. Given that "genetic diversity provides the basis for all plant improvement," 12 the study of different genetic varieties of crop (genomics) and how well they grow in different environmental conditions (phenomics) is critical to meet this challenge. Each year, around the world, millions of agricultural crops (such as grains and legumes) with different genetic profiles are grown in the field, subjected to different environmental factors (e.g., exposed to disease, herbicides, water stress, etc.) and the physical response of the plants (e.g., how tolerant they are, how much yield they produce) is measured. The process is repeated annually, driving plant productivity and adaptability forward, however, advances in genomics have not been matched by similar advances in phenomics and the ability to obtain these physical measurements is considered to be the major bottleneck. 2,12,14 Crop characteristics (phenotype t...

Anecdotal evidence identified a change in the reaction of the resistant lentil cv Nipper to ascochyta blight in South Australia in 2010 and subsequent seasons, leading to infection. This study investigated field reactions of lentil cultivars against Ascochyta lentis and the pathogenic variability of the A. lentis population in southern Australia on commonly grown cultivars and on parental germplasm used in the Australian lentil breeding program. Disease data recorded in agronomic and plant breeder field trials from 2005 to 2014 in southern Australia confirmed the change in reaction on the foliage of the previously resistant cvs Nipper and Northfield. Cultivar responses to seed staining from A. lentis did not change. The change in foliar response was confirmed in a series of controlled environment experiments using single, conidium-derived, isolates of A. lentis collected over different years and inoculated onto differential host sets. Specific isolate/cultivar interactions produced a significant range of disease reactions from high to low aggressiveness with a greater percentage of isolates more aggressive on cvs Nipper, Northfield and PBA Flash than previously detected. Specific isolates were tested against Australian lentil cultivars and breeding lines in controlled conditions, again verifying the aggressiveness on cv Nipper. A small percentage of isolates collected prior to the commercial release of cv Nipper were also able to infect this cultivar indicating a natural variability of the A. lentis population which subsequently may have been selected in response to high cropping intensity of cv Nipper. Spore release studies from naturally infested lentil stubbles collected from commercial crops also resulted in a high percentage of infection on the previously resistant cvs Nipper and Northfield. Less than 10% of the lesions developed on the resistant differentials ILL7537 and cv Indianhead. Pathogenic variation within the seasonal populations was not affected by the cultivar from which the stubble was sourced, further indicating a natural variability in aggressiveness. The impact of dominant cultivars in cropping systems and loss of effective disease resistance is discussed. Future studies are needed to determine if levels of aggressiveness among A. lentis isolates are increasing against a range of elite cultivars.

Lentil (Lens culinaris) is an important pulse crop in the southern and western cropping zones. Weed management can be difficult in lentil because of its poor early growth and the limited range of safe selective post-emergent herbicides available. Experiments were conducted at Minlaton, South Australia, and Horsham, Victoria, to examine the effects of early vigour on the ability of lentil to compete against a broadleaf weed. Early growth in lentil was manipulated by selecting genotypes with different levels of early vigour and by using a range of sowing rates. Canola (Brassica napus cv. Beacon) was used to mimic the growth of a cruciferous weed and it was sown at 0, 0.25 (Minlaton only), 0.5, 1 or 2 kg/ha. Lentil genotypes were selected that represented the range in early vigour currently available within the breeding program. Another experiment examined the effect of plant density of lentils with different degrees of early vigour on the yield of canola. Grain yield of lentil declined as the density of canola increased. The initial reduction in canola yield over sites was about 4%/plant.m2, but was as high as 12%/plant.m2 in one treatment. This yield loss is similar to that reported for other grain legume crops, but is much higher than the initial yield loss reported for wheat. The differences in early vigour between genotypes were insufficient to affect the competitive ability of lentil. In contrast, increasing the sowing rate of lentil significantly reduced canola grain yield and increased lentil grain yield. When the density of canola was low (10 plants/m2), doubling the lentil plant density to 200 plants/m2 limited the yield loss to 10%. The results suggest the level of variation in early vigour among the present genotypes is insufficient to increase the competitive ability of the crop. Increasing the plant population of lentil is a more effective means of suppressing weed growth and increasing grain yield.

Field pea (Pisum sativum L.

There is a large gap between the refined approaches to characterise genotypes and the common use of location and season as a coarse surrogate for environmental characterisation of breeding trials. As a framework for breeding, the aim of this paper is quantifying the spatial and temporal patterns of thermal and water stress for field pea in Australia. We compiled a dataset for yield of the cv. Kaspa measured in 185 environments, and investigated the associations between yield and seasonal patterns of actual temperature and modelled water stress.Correlations between yield and temperature indicated two distinct stages. In the first stage, during crop establishment and canopy expansion before flowering, yield was positively associated with minimum temperature. Mean minimum temperature below~78C suggests that crops were under suboptimal temperature for both canopy expansion and radiationuse efficiency during a significant part of this early growth period. In the second stage, during critical reproductive phases, grain yield was negatively associated with maximum temperature over 258C.Correlations between yield and modelled water supply/demand ratio showed a consistent pattern with three phases: no correlation at early stages of the growth cycle, a progressive increase in the association that peaked as the crop approached the flowering window, and a progressive decline at later reproductive stages. Using long-term weather records and modelled water stress for 104 locations, we identified three major patterns of water deficit nation wide. Environment type 1 (ET1) represents the most favourable condition, with no stress during most of the preflowering phase and gradual development of mild stress after flowering. Type 2 is characterised by increasing water deficit between 400 degree-days before flowering and 200 degree-days after flowering and rainfall that relieves stress late in the season. Type 3 represents the more stressful condition with increasing water deficit between 400 degree-days before flowering and maturity. Across Australia, the frequency of occurrence was 24% for ET1, 32% for ET2 and 43% for ET3, highlighting the dominance of the most stressful condition. Actual yield averaged 2.2 t/ha for ET1, 1.9 t/ha for ET2 and 1.4 t/ha for ET3, and the frequency of each pattern varied substantially among locations. Shifting from a nominal (i.e. location and season) to a quantitative (i.e. stress type) characterisation of environments could help improving breeding efficiency of field pea in Australia.