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...

A critical step in treating or eradicating weed infestations amongst vegetable crops is the ability to accurately and reliably discriminate weeds from crops. In recent times, high spatial resolution hyperspectral imaging data from ground based platforms have shown particular promise in this application. Using spectral vegetation signatures to discriminate between crop and weed species has been demonstrated on several occasions in the literature over the past 15 years. A number of authors demonstrated successful per-pixel classification with accuracies of over 80%. However, the vast majority of the related literature uses supervised methods, where training datasets have been manually compiled. In practice, static training data can be particularly susceptible to temporal variability due to physiological or environmental change. A self-supervised training method that leverages prior knowledge about seeding patterns in vegetable fields has recently been introduced in the context of RGB imaging, allowing the classifier to continually update weed appearance models as conditions change. This paper combines and extends these methods to provide a selfsupervised framework for hyperspectral crop/weed discrimination with prior knowledge of seeding patterns using an autonomous mobile ground vehicle. Experimental results in corn crop rows demonstrate the system's performance and limitations.

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