Literature DB >> 31003607

What is cost-efficient phenotyping? Optimizing costs for different scenarios.

Daniel Reynolds1, Frederic Baret2, Claude Welcker3, Aaron Bostrom1, Joshua Ball1, Francesco Cellini4, Argelia Lorence5, Aakash Chawade6, Mehdi Khafif7, Koji Noshita8, Mark Mueller-Linow9, Ji Zhou10, François Tardieu11.   

Abstract

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. Crown
Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Affordable; Cost; Imaging; Information system; Phenomics; Phenotyping

Mesh:

Year:  2018        PMID: 31003607     DOI: 10.1016/j.plantsci.2018.06.015

Source DB:  PubMed          Journal:  Plant Sci        ISSN: 0168-9452            Impact factor:   4.729


  24 in total

1.  Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data.

Authors:  Gota Morota; Diego Jarquin; Malachy T Campbell; Hiroyoshi Iwata
Journal:  Methods Mol Biol       Date:  2022

Review 2.  New approaches to improve crop tolerance to biotic and abiotic stresses.

Authors:  Miguel González Guzmán; Francesco Cellini; Vasileios Fotopoulos; Raffaella Balestrini; Vicent Arbona
Journal:  Physiol Plant       Date:  2021-09-17       Impact factor: 5.081

Review 3.  Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.

Authors:  Jacob I Marsh; Haifei Hu; Mitchell Gill; Jacqueline Batley; David Edwards
Journal:  Theor Appl Genet       Date:  2021-04-14       Impact factor: 5.699

4.  Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.

Authors:  Mahlet T Anche; Nicholas S Kaczmar; Nicolas Morales; James W Clohessy; Daniel C Ilut; Michael A Gore; Kelly R Robbins
Journal:  Theor Appl Genet       Date:  2020-07-01       Impact factor: 5.699

Review 5.  Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.

Authors:  Giao N Nguyen; Sally L Norton
Journal:  Plants (Basel)       Date:  2020-06-29

6.  A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding.

Authors:  Maxime Ryckewaert; Daphné Héran; Emma Faur; Pierre George; Bruno Grèzes-Besset; Frédéric Chazallet; Yannick Abautret; Myriam Zerrad; Claude Amra; Ryad Bendoula
Journal:  Sensors (Basel)       Date:  2020-08-18       Impact factor: 3.576

7.  Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment.

Authors:  Silvere Vialet-Chabrand; Tracy Lawson
Journal:  J Exp Bot       Date:  2019-05-09       Impact factor: 6.992

8.  Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production.

Authors:  Alan Bauer; Aaron George Bostrom; Joshua Ball; Christopher Applegate; Tao Cheng; Stephen Laycock; Sergio Moreno Rojas; Jacob Kirwan; Ji Zhou
Journal:  Hortic Res       Date:  2019-06-01       Impact factor: 6.793

9.  Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping.

Authors:  Stuart A Bagley; Jonathan A Atkinson; Henry Hunt; Michael H Wilson; Tony P Pridmore; Darren M Wells
Journal:  Sensors (Basel)       Date:  2020-06-11       Impact factor: 3.576

Review 10.  Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits.

Authors:  Keiichi Mochida; Ryuei Nishii; Takashi Hirayama
Journal:  Plant Cell Physiol       Date:  2020-08-01       Impact factor: 4.927

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