Review: Application of Artificial Intelligence in Phenomics

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2021 Jul 2

PMID 34202291

Citations 15

Authors

Shona Nabwire

Hyun-Kwon Suh

Moon S Kim

Insuck Baek

Byoung-Kwan Cho

Affiliations

Soon will be listed here.

Abstract

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

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References

Ehlert B, Hincha D . Chlorophyll fluorescence imaging accurately quantifies freezing damage and cold acclimation responses in Arabidopsis leaves. Plant Methods. 2008; 4:12. PMC: 2430023. DOI: 10.1186/1746-4811-4-12. View

Montes J, Melchinger A, Reif J . Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 2007; 12(10):433-6. DOI: 10.1016/j.tplants.2007.08.006. View

Rahaman M, Ahsan M, Chen M . Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification. Sci Rep. 2019; 9(1):19526. PMC: 6925301. DOI: 10.1038/s41598-019-55609-6. View

Kuhlgert S, Austic G, Zegarac R, Osei-Bonsu I, Hoh D, Chilvers M . MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. R Soc Open Sci. 2016; 3(10):160592. PMC: 5099005. DOI: 10.1098/rsos.160592. View

Chaerle L, Pineda M, Romero-Aranda R, Van Der Straeten D, Baron M . Robotized thermal and chlorophyll fluorescence imaging of pepper mild mottle virus infection in Nicotiana benthamiana. Plant Cell Physiol. 2006; 47(9):1323-36. DOI: 10.1093/pcp/pcj102. View

Furbank R, Tester M . Phenomics--technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011; 16(12):635-44. DOI: 10.1016/j.tplants.2011.09.005. View

Chow T, Tan K, Ng B, Razul S, Tay C, Chia T . Diagnosis of virus infection in orchid plants with high-resolution optical coherence tomography. J Biomed Opt. 2009; 14(1):014006. DOI: 10.1117/1.3066900. View

Chaerle L, Van Der Straeten D . Imaging techniques and the early detection of plant stress. Trends Plant Sci. 2000; 5(11):495-501. DOI: 10.1016/s1360-1385(00)01781-7. View

Buzzy M, Thesma V, Davoodi M, Mohammadpour Velni J . Real-Time Plant Leaf Counting Using Deep Object Detection Networks. Sensors (Basel). 2020; 20(23). PMC: 7730908. DOI: 10.3390/s20236896. View

10.

Ghosal S, Blystone D, Singh A, Ganapathysubramanian B, Singh A, Sarkar S . An explainable deep machine vision framework for plant stress phenotyping. Proc Natl Acad Sci U S A. 2018; 115(18):4613-4618. PMC: 5939070. DOI: 10.1073/pnas.1716999115. View

11.

Cooper C, Shirk J, Zuckerberg B . The invisible prevalence of citizen science in global research: migratory birds and climate change. PLoS One. 2014; 9(9):e106508. PMC: 4153593. DOI: 10.1371/journal.pone.0106508. View

12.

Pound M, Atkinson J, Townsend A, Wilson M, Griffiths M, Jackson A . Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience. 2017; 6(10):1-10. PMC: 5632296. DOI: 10.1093/gigascience/gix083. View

13.

Atkinson J, Pound M, Bennett M, Wells D . Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol. 2018; 55:1-8. PMC: 6378649. DOI: 10.1016/j.copbio.2018.06.002. View

14.

Walter T, Shattuck D, Baldock R, Bastin M, Carpenter A, Duce S . Visualization of image data from cells to organisms. Nat Methods. 2010; 7(3 Suppl):S26-41. PMC: 3650473. DOI: 10.1038/nmeth.1431. View

15.

Sabanci K, Kayabasi A, Toktas A . Computer vision-based method for classification of wheat grains using artificial neural network. J Sci Food Agric. 2016; 97(8):2588-2593. DOI: 10.1002/jsfa.8080. View

16.

Prey L, von Bloh M, Schmidhalter U . Evaluating RGB Imaging and Multispectral Active and Hyperspectral Passive Sensing for Assessing Early Plant Vigor in Winter Wheat. Sensors (Basel). 2018; 18(9). PMC: 6163924. DOI: 10.3390/s18092931. View

17.

Brichet N, Fournier C, Turc O, Strauss O, Artzet S, Pradal C . A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods. 2017; 13:96. PMC: 5688816. DOI: 10.1186/s13007-017-0246-7. View

18.

Shakoor N, Lee S, Mockler T . High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol. 2017; 38:184-192. DOI: 10.1016/j.pbi.2017.05.006. View

19.

Ac A, Malenovsky Z, Hanus J, Tomaskova I, Urban O, Marek M . Near-distance imaging spectroscopy investigating chlorophyll fluorescence and photosynthetic activity of grassland in the daily course. Funct Plant Biol. 2020; 36(11):1006-1015. DOI: 10.1071/FP09154. View

20.

Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford M . Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol. 2020; 44(1):143-153. DOI: 10.1071/FP16163. View