TY - JOUR
T1 - What is cost-efficient phenotyping? Optimizing costs for different scenarios
AU - Reynolds, Daniel
AU - Baret, Frederic
AU - Welcker, Claude
AU - Bostrom, Aaron
AU - Ball, Joshua
AU - Cellini, Francesco
AU - Lorence, Argelia
AU - Chawade, Aakash
AU - Khafif, Mehdi
AU - Noshita, Koji
AU - Mueller-Linow, Mark
AU - Zhou, Ji
AU - Tardieu, François
N1 - Funding Information:
Authors are grateful to the phenotyping communities in the French and UK phenotyping infrastructures for cost evaluation. FT, CW and FB thank the ANR-PIA project PHENOME EMPHASIS.FR (ANR-11-INBS-0012) and EPPN 2020 (UE H2020 grant agreement No 731013) for partly funding this work. JZ, DR, AB and JB are funded by the Biotechnology and Biological Sciences Research Council (BBSRC), Core Strategic Programme Grant (BB/CSP17270/1) at EI, BBSRC’s Designing Future Wheat Strategic Programme (BB/P016855/1) to G. Moore and (BBS/E/T/000PR9785) to JZ.
Publisher Copyright:
© 2018
PY - 2019/5
Y1 - 2019/5
N2 - Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.
AB - Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.
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U2 - 10.1016/j.plantsci.2018.06.015
DO - 10.1016/j.plantsci.2018.06.015
M3 - Article
C2 - 31003607
AN - SCOPUS:85051008045
SN - 0168-9452
VL - 282
SP - 14
EP - 22
JO - Plant Science
JF - Plant Science
ER -