25Progress in remote sensing and robotic technologies decreases the hardware costs of 26 phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, 27 and present a trade-off between investment and manpower costs. We then discuss the structure 28 of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and 29 infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) 30 major costs arise from plant handling and manpower; (ii) the total costs per pot/microplot are 31 similar in robotized platform or field experiments with drones, hand-held or robotized ground 32 vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These 33 conclusions depend on the context, in particular for labor cost, the quantitative demand of 34 phenotyping and the number of days available for phenotypic measurements due to climatic 35 constraints. Data analysis represents 10-20% of total cost if pipelines have already been 36 developed. A trade-off exists between the initial high cost of pipeline development and labor cost 37 of manual operations. Overall, depending on the context and objectives, "cost-effective" 38 phenotyping may involve either low investment ("affordable phenotyping"), or initial high 39 investments in sensors, vehicles and pipelines that result in higher quality and lower operational 40 costs. 41 Highlights 42 -New technologies considerably reduce the costs of sensors and automated vehicles 43 -Low investment in sensors, vehicles or pipelines present trade-offs with labor costs 44 -Plant/plot handling and labor costs represent the major proportion of costs in phenotyping 45 experiments 46 -The costs of high-throughput experiments in the field and in automated platforms is similar 47 regardless of vehicles 48 -The development of software applications (e.g. imaging, phenotypic analyses, models, 49 information system) is a major part of costs 50 51 52 54 I Imaging techniques with a range of hardware costs 55 1.1 Handheld phenotyping technologies 56 1.2 Aerial imaging for large-scale phenotyping 57 1.3 Imaging with ground vehicles 58 1.4 Environmental measurements 59 II Costs associated with image capture represent a fraction of the overall cost of phenotyping 60 2.1 A method for calculating costs in field and greenhouse platforms 61 2.2 A high cost for plant management 62 2.3 Investing in appropriate environmental characterization results in comparatively low cost 63 for a high return 64 2.4 Imaging costs: a trade-off between investment and labor costs 65 2.4.1 The choice of vehicle mostly depends on the demand for microplots per year 66 2.4.2 The cost of imaging devices is similar to that of vehicles that carry sensors 67 2.5 Costs of typical experiments 68 2.5.1 Image analysis: a tradeoff between investment in automated workflows and day-to-day 69 labor costs 70 2.5.2 High costs for data analysis for the identification of traits 71 2.5.3 Costs associated with data storag...

Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection.

Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf- Lettuce , is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution across the field, based on which associated global positioning system (GPS) tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination-and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.

Identifying genetic variation that increases crop yields is a primary objective in plant breeding. We used association analyses of oilseed rape/canola (Brassica napus) accessions to identify genetic variation that influences seed size, lipid content, and final crop yield. Variation in the promoter region of the HECT E3 ligase gene BnaUPL3.C03 made a major contribution to variation in seed weight per pod, with accessions exhibiting high seed weight per pod having lower levels of BnaUPL3.C03 expression. We defined a mechanism in which UPL3 mediated the proteasomal degradation of LEC2, a master transcriptional regulator of seed maturation. Accessions with reduced UPL3 expression had increased LEC2 protein levels, larger seeds, and prolonged expression of lipid biosynthetic genes during seed maturation. Natural variation in BnaUPL3.C03 expression appears not to have been exploited in current B. napus breeding lines and could therefore be used as a new approach to maximize future yields in this important oil crop.

BackgroundHigh-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales.FindingsCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted.ConclusionsAs a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.

Identifying genetic variation that increases crop yields is a primary objective in plant breeding. We have used association analyses of Brassica napus (oilseed rape/canola) accessions to identify variation in the expression of a HECT E3 ligase gene, BnaUPL3.C03, that influences seed size and final yield. We establish a mechanism in which UPL3 mediates the proteasomal degradation of LEC2, a master regulator of seed maturation. Reduced UPL3 expression increases LEC2 protein levels and prolongs expression of lipid biosynthetic genes and seed maturation. Natural variation in BnaUPL3.C03 expression has not yet been exploited in current Brassica napus breeding lines and can therefore be used as a new approach to maximize future yields in this important oil crop.

Background The same species of plant can exhibit very diverse sizes and shapes of organs that are genetically determined. Characterising genetic variation underlying this morphological diversity is an important objective in evolutionary studies and it also helps identify the functions of genes influencing plant growth and development. Extensive screens of mutagenised Arabidopsis populations have identified multiple genes and mechanisms affecting organ size and shape, but relatively few studies have exploited the rich diversity of natural populations to identify genes involved in growth control. Results We screened a relatively well characterised collection of Arabidopsis thaliana accessions for variation in petal size. Association analyses identified sequence and gene expression variation on chromosome 4 that made a substantial contribution to differences in petal area. Variation in the expression of a previously uncharacterised gene At4g16850 (named as KSK) had a substantial role on variation in organ size by influencing cell size. Over-expression of KSK led to larger petals with larger cells and promoted the formation of stamenoid features. The expression of auxin-responsive genes known to limit cell growth was reduced in response to KSK over-expression. ANT expression was also reduced in KSK over-expression lines, consistent with altered floral identities. Auxin responses were reduced in KSK over-expressing cells, consistent with changes in auxin-responsive gene expression. KSK may therefore influence auxin responses during petal development. Conclusions Understanding how genetic variation influences plant growth is important for both evolutionary and mechanistic studies. We used natural populations of Arabidopsis thaliana to identify sequence variation in a promoter region of Arabidopsis accessions that mediated differences in the expression of a previously uncharacterised membrane protein. This variation contributed to altered auxin responses and cell size during petal growth.