High-Throughput Plant Phenotyping for Developing Novel Biostimulants: From Lab to Field or From Field to Lab?
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
30154818
PubMed Central
PMC6102389
DOI
10.3389/fpls.2018.01197
Knihovny.cz E-zdroje
- Klíčová slova
- bioassaying, functional characterization, high-throughput screening, imaging methods, integrative phenotyping, mode of action, morpho-physiological traits, nutrient use efficiency,
- Publikační typ
- časopisecké články MeSH
Plant biostimulants which include bioactive substances (humic acids, protein hydrolysates and seaweed extracts) and microorganisms (mycorrhizal fungi and plant growth promoting rhizobacteria of strains belonging to the genera Azospirillum, Azotobacter, and Rhizobium spp.) are gaining prominence in agricultural systems because of their potential for improving nutrient use efficiency, tolerance to abiotic stressors, and crop quality. Highly accurate non-destructive phenotyping techniques have attracted the interest of scientists and the biostimulant industry as an efficient means for elucidating the mode of biostimulant activity. High-throughput phenotyping technologies successfully employed in plant breeding and precision agriculture, could prove extremely useful in unraveling biostimulant-mediated modulation of key quantitative traits and would also facilitate the screening process for development of effective biostimulant products in controlled environments and field conditions. This perspective article provides an innovative discussion on how small, medium, and large high-throughput phenotyping platforms can accelerate efforts for screening numerous biostimulants and understanding their mode of action thanks to pioneering sensor and image-based phenotyping techniques. Potentiality and constraints of small-, medium-, and large-scale screening platforms are also discussed. Finally, the perspective addresses two screening approaches, "lab to field" and "field to lab," used, respectively, by small/medium and large companies for developing novel and effective second generation biostimulant products.
AgroBioChem s r o Bystročice Czechia
Department of Agricultural and Forestry Sciences University of Tuscia Viterbo Italy
Department of Agricultural Sciences University of Naples Federico 2 Portici Italy
Zobrazit více v PubMed
Araus J. L., Cairns J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. PubMed DOI
Bonini P., Long D. H., Canaguier R., Colla G., Leman J. (2017).
Calvo P., Nelson L., Kloepper J. W. (2014). Agricultural uses of plant biostimulants. DOI
Casa R., Castaldi F., Pascucci S., Basso B., Pignatti S. (2013). Geophysical and hyperspectral data fusion techniques for in-field estimation of soil properties. DOI
Colla G., Rouphael Y. (2015). Biostimulants in horticulture. DOI
Colla G., Rouphael Y., Bonini P., Cardarelli M. (2015). Coating seeds with endophytic fungi enhances growth, uptake nutrient, yield and grain quality of winter wheat.
De Diego N., Fürst T., Humplík J. F., Ugena L., Podlešáková K., Spíchal L. (2017). An automated method for high-throughput screening of Arabidopsis rosette growth in multi-well plates and Its validation in stress conditions. PubMed DOI PMC
Deery D., Jimenez-Berni J., Jones H., Sirault X., Furbank R. (2014). Proximal remote sensing buggies and potential applications for field-based phenotyping. DOI
du Jardin P. (2015). Plant biostimulants: definition, concept, main categories and regulation. DOI
Enciso J., Maeda M., Landivar J., Jung J., Chang A. (2017). A ground based platform for high throughput phenotyping. DOI
Ertani A., Pizzeghello D., Francioso O., Sambo P., Sanchez-Cortes S., Nardi S. (2014). PubMed DOI PMC
Ertani A., Schiavon M., Muscolo A., Nardi S. (2013). Alfalfa plant-derived biostimulant stimulate short-term growth of salt stressed Zea mays L. plants.
Friedli M., Kirchgessner N., Grieder C., Liebisch F., Mannale M., Walter A. (2016). Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. PubMed DOI PMC
Halder V., Kombrink E. (2015). Facile high-throughput forward chemical genetic screening by in situ monitoring of glucuronidase-based reporter gene expression in Arabidopsis thaliana. PubMed DOI PMC
Humplík J. F., Lazár D., Husičková A., Spíchal L. (2015). Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review. PubMed DOI PMC
Jones H. G., Hutchinson P. A., May T., Jamali H., Deery D. M. (2018). A practical method using a network of fixed infrared sensors for estimating crop canopy conductance and evaporation rate. DOI
Kirchgessner N., Liebisch F., Yu K., Pfeifer J., Friedli M., Hund A., et al. (2017). The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. PubMed
Kjaer K. H., Ottosen C. O. (2015). 3D laser triangulation for plant phenotyping in challenging environments. PubMed DOI PMC
Lucini L., Rouphael Y., Cardarelli M., Bonini P., Baffi C., Colla G. (2018). A vegetal biopolymer-based biostimulant promoted root growth in melon while triggering brassinosteroids and stress-related compounds. PubMed DOI PMC
Lucini L., Rouphael Y., Cardarelli M., Canaguier R., Kumar P., Colla G. (2015). The effect of a plant-derived protein hydrolysate on metabolic profiling and crop performance of lettuce grown under saline conditions. DOI
Madec S., Baret F., De Solan B., Thomas S., Dutartre D., Jezequel S., et al. (2017). High-throughput phenotyping of plant height: comparing unmanned aerial vehicles and ground lidar estimates. PubMed DOI PMC
Mishra K. B., Mishra A., Klem K., Govindjee (2016). Plant phenotyping: a perspective. DOI
Mishra P., Asaari M. S. M., Herrero-Langreo A., Lohumi S., Diezma B., Scheunders P. (2017). Close range hyperspectral imaging of plants: a review. DOI
Naito H., Ogawa S., Valencia M. O., Mohri H., Urano Y., Hosoi F., et al. (2017). Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. DOI
Paez-Garcia A., Motes C. M., Scheible W. R., Chen R., Blancaflor E. B., Monteros M. J. (2015). Root traits and phenotyping strategies for plant improvement. PubMed DOI PMC
Pauli D., Chapman S. C., Bart R., Topp C. N., Lawrence-Dill C. J., Poland J., et al. (2016). The quest for understanding phenotypic variation via integrated approaches in the field environment. PubMed DOI PMC
Petrozza A., Santaniello A., Summerer S., Di Tommaso G., Di Tommaso D., Paparelli E., et al. (2014). Physiological responses to Megafol treatments in tomato plants under drought stress: a phenomic and molecular approach. DOI
Rahaman M. M., Chen D., Gillani Z., Klukas C., Chen M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. PubMed DOI PMC
Rodriguez-Furlán C., Miranda G., Reggiardo M., Hicks G. R., Norambuena L. (2016). High throughput selection of novel plant growth regulators: assessing the translatability of small bioactive molecules from Arabidopsis to crops. PubMed DOI
Rouphael Y., Colla G., Giordano M., El-Nakhel C., Kyriacou M. C., De Pascale S. (2017). Foliar applications of a legume-derived protein hydrolysate elicit dose dependent increases of growth, leaf mineral composition, yield and fruit quality in two greenhouse tomato cultivars. DOI
Salas Fernandez M. G., Bao Y., Tang L., Schnable P. S. (2017). A high-throughput, field-based phenotyping technology for tall biomass crops. PubMed DOI PMC
Sankaran S., Khot L. R., Espinoza C. Z., Jarolmasjed S., Sathuvalli V. R., Vandemark G. J., et al. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. DOI
Shakoor N., Lee S., Mockler T. C. (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. PubMed DOI
Tardieu F., Cabrera-Bosquet L., Pridmore T., Bennett M. (2017). Plant phenomics, from sensors to knowledge. PubMed DOI
Virlet N., Sabermanesh K., Sadeghi-Tehran P., Hawkesford M. J. (2017). Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. PubMed DOI
Williams D., Britten A., McCallum S., Jones H. G., Aitkenhead M., Karley A., et al. (2017). A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions. PubMed DOI PMC
Yang G., Liu J., Zhao C., Li Z., Huang Y., Yu H., et al. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. PubMed DOI PMC
Zhang X., Schmidt R. E. (1997). The impact of growth regulators on alpha-tocopherol status of water-stressed
Presence and future of plant phenotyping approaches in biostimulant research and development
Functional phenomics for improved climate resilience in Nordic agriculture