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.