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A short review of RGB sensor applications for accessible high-throughput phenotyping

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Journal of Crop Science and Biotechnology Aims and scope Submit manuscript

Abstract

Present challenge on high-throughput phenotyping is the major task for food production. However, many researchers have issues on operating prompt phenotyping that effectively reduces the breeding cycle in reason of requiring various ranges of specialists of technologies, and expensive sensors and instruments. While among those, the RGB (red, green, blue) sensor and its applications allow a relatively affordable and accessible process, and maintain the beneficial features of high-throughput phenotyping methods. This sensor enables the image analysis with solitary application, which provides superficial phenotype, and feasible as a supplementary sensor for multi-/hyperspectral data correction, which segmentate the target crop from image. Thus, we suggest the RGB sensors as an alternative to provide researchers helpful methods for low-priced and manageable high-throughput phenotyping.

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References

  • Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61

    Article  CAS  PubMed  Google Scholar 

  • Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23:451–466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baek J, Lee E, Kim N, Kim SL, Choi I, Ji H, Chung YS, Choi MS, Moon JK, Kim KH (2020) High throughput phenotyping for various traits on soybean seeds using image analysis. Sensors 20:248

    Article  PubMed Central  Google Scholar 

  • Barot ZR, Limbad N (2015) An approach for detection and classification of fruit disease: a survey. Int J Sci Res 4:838–842

    Google Scholar 

  • Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G (2014) Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens 6:10395–10412

    Article  Google Scholar 

  • Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp M, Bareth G (2015) Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf 39:79–87

    Google Scholar 

  • Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Luis AJ (2012) High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge F. J Integr Plant Biol 54:312–320

    Article  PubMed  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252

    Article  Google Scholar 

  • Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 2014(4):349–379

    Article  Google Scholar 

  • Diago MP, Correa C, Millán B, Barreiro P, Valero C, Tardaguila J (2012) Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions. Sensors 12:16988–17006

    Article  PubMed  PubMed Central  Google Scholar 

  • Fernandez-Gallego JA, Kefauver SC, Vatter T, Gutiérrez NA, Nieto-Taladriz MT, Araus JL (2019) Low-cost assessment of grain yield in durum wheat using RGB images. Eur J Agron 105:146–156

    Article  Google Scholar 

  • Fernandez-Gallego JA, Lootens P, Borra-Serrano I, Derycke V, Haesaert G, Roldán-Ruiz I, Araus JL, Kefauver SC (2020) Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant J 103:1603–1613

    Article  CAS  PubMed  Google Scholar 

  • Fu L, Gao F, Wu J, Li R, Karkee M, Zhang Q (2020) Application of consumer RGB-D cameras for fruit detection and localization in field: a critical review. Comput Electron Agric 177:105687

    Article  Google Scholar 

  • Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44

    Article  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58:289–298

    Article  Google Scholar 

  • Henry P, Krainin M, Herbst E, Ren X, Fox D (2014) RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In: Khatib O, Kumar V, Sukhatme G (eds) Experimental robotics, 1st edn. Springer, Berlin, pp 477–491

    Chapter  Google Scholar 

  • Holland KH, Lamb DW, Schepers JS (2012) Radiometry of proximal active optical sensors (AOS) for agricultural sensing. IEEE J STARS 5:1793–1802

    Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  • Jiang Y, Li C, Paterson AH, Sun S, Xu R, Robertson J (2018) Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera. Front Plant Sci 8:2233

    Article  PubMed  PubMed Central  Google Scholar 

  • Jin X, Liu S, Baret F, Hemerlé M, Comar A (2017) Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ 198:105–114

    Article  Google Scholar 

  • Kim DW, Yun HS, Jeong SJ, Kwon YS, Kim SG, Lee WS, Kim HJ (2018) Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery. Remote Sens 10:563–587

    Article  Google Scholar 

  • Klodt M, Herzog K, Töpfer R, Cremers D (2015) Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinform 16:1–11

    Article  Google Scholar 

  • Kuska MT, Mahlein AK (2018) Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. Eur J Plant Pathol 152:987–992

    Article  Google Scholar 

  • Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87

    Article  CAS  PubMed  Google Scholar 

  • Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111

    Article  PubMed  PubMed Central  Google Scholar 

  • Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:1–20

    Article  Google Scholar 

  • Linker R, Cohen O, Naor A (2012) Determination of the number of green apples in RGB images recorded in orchards. Comput Electron Agric 81:45–57

    Article  Google Scholar 

  • Liu S, Baret F, Andrieu B, Burger P, Hemmerlé M (2017) Estimation of wheat plant density at early stages using high resolution imagery. Front Plant Sci 8:1–10

    Google Scholar 

  • Maimaitijiang M, Ghulam A, Sidike P, Hartling S, Maimaitiyiming M, Peterson K, Shavers E, Fishman J, Peterson J, Kadam S, Burken J, Fritschi F (2017) Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS J Photogramm Remote Sens 134:43–58

    Article  Google Scholar 

  • Malik MH, Qiu R, Yang G, Zhang M, Li H, Li M (2019) Tomato segmentation and localization method based on RGB-D camera. Int Agric Eng J 28:278–287

    Google Scholar 

  • Mehrabi Z, Ramankutty N (2019) Synchronized failure of global crop production. Nat Ecol Evol 3:780–786

    Article  PubMed  Google Scholar 

  • Meyer GE, Neto JC (2008) Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric 63:282–293

    Article  Google Scholar 

  • Milella A, Marani R, Petitti A, Reina G (2019) In-field high throughput grapevine phenotyping with a consumer-grade depth camera. Comput Electron Agric 156:293–306

    Article  Google Scholar 

  • Qaim M (2020) Role of new plant breeding technologies for food security and sustainable agricultural development. Appl Econ Persp Policy 42:129–150

    Article  Google Scholar 

  • Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48:119–126

    Article  Google Scholar 

  • Rezzouk FZ, Gracia-Romero A, Kefauver SC, Gutiérrez NA, Aranjuelo I, Serret MD, Araus JL (2020) Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: Effects of growing temperature and vernalization. Plant Sci 295:110281

    Article  CAS  PubMed  Google Scholar 

  • Rueda-Ayala VP, Peña JM, Höglind M, Bengochea-Guevara JM, Andújar D (2019) Comparing UAV-based technologies and RGB-D reconstruction methods for plant height and biomass monitoring on grass ley. Sensors 19:535

    Article  PubMed Central  Google Scholar 

  • Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, Reynolds M, Singh R (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 Genes Genom Genet 6:2799–2808

    Google Scholar 

  • Salas Fernandez MG, Bao Y, Tang L, Schnable PS (2017) A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol 174:2008–2022

    Article  PubMed  PubMed Central  Google Scholar 

  • Sarkar D, Kar SK, Chattopadhyay A, Shikha, Rakshit A, Tripathi VK, Dubey PK, Abhilash PC (2020) Low input sustainable agriculture: a viable climate-smart option for boosting food production in a warming world. Ecol Ind 115:106412

    Article  Google Scholar 

  • Scharr H, Minervini M, French AP, Klukas C, Kramer DM, Liu X, Luengo I, Pate J, Polder G, Vukadinovic D, Yin X, Tsaftaris SA (2016) Leaf segmentation in plant phenotyping: a collation study. Mach Vis Appl 27:585–606

    Article  Google Scholar 

  • Shafiekhani A, Kadam S, Fritschi FB, DeSouza GN (2017) Vinobot and vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors 17:214

    Article  PubMed Central  Google Scholar 

  • Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A (2021) A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Comput Electron Agric 183:106042

    Article  Google Scholar 

  • Tamburino L, Bravo G, Clough Y, Nicholas KA (2020) From population to production: 50 years of scientific literature on how to feed the world. Glob Food Sec 24:100346

    Article  Google Scholar 

  • Vincini M, Frazzi E, D’Alessio P (2008) A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis Agric 9:303–319

    Article  Google Scholar 

  • Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford MJ (2016) Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44:143–153

    Article  PubMed  Google Scholar 

  • Vit A, Shani G (2018) Comparing RGB-D sensors for close range outdoor agricultural phenotyping. Sensors 18:4413

    Article  PubMed Central  Google Scholar 

  • Xia C, Wang L, Chung BK, Lee JM (2015) In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation. Sensors 15:20463–20479

    Article  PubMed  PubMed Central  Google Scholar 

  • Zanuttigh P, Marin G, Dal Mutto C, Dominio F, Minto L, Cortelazzo GM (2016) Operating principles of depth cameras. In: Zanuttigh P, Marin G, Dal Mutto C, Dominio F, Minto L, Cortelazzo GM (eds) Time-of-flight and structured light depth cameras, 2nd edn. Springer, Cham, New York City, pp 43–113

    Google Scholar 

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Acknowledgements

This work was carried out with the support of “Cooperative Research Program for Agriculture Science & Technology Development (Development of an approach to analyze timing and locality of migratory pests occurrence for rice using information derived from an automated surveillance system: PJ0158702021)” Rural Development Administration, Republic of Korea. Also, we are grateful to Sustainable Agricultural Research Institute (SARI) in Jeju National University for providing the experimental facilities.

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  1. Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea

    JaeYoung Kim & Yong Suk Chung

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  1. JaeYoung Kim
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Correspondence to Yong Suk Chung.

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The authors declare that the research was conducted in the absence of any commercial or fnancial relationships that could be construed as a potential confict of interest.

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Kim, J., Chung, Y.S. A short review of RGB sensor applications for accessible high-throughput phenotyping. J. Crop Sci. Biotechnol. 24, 495–499 (2021). https://doi.org/10.1007/s12892-021-00104-6

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