A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

Abdulridha J, Ampatzidis Y, Kakarla SC, Roberts P (2020) Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agric 21:955–978. https://doi.org/10.1007/s11119-019-09703-4 Adam E, Deng H, Odindi J et al (2017) Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm. J Spectrosc 2017:1–8. https://doi.org/10.1155/2017/6961387 Al-Tamimi N, Brien C, Oakey H et al (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat Commun 7:13342. https://doi.org/10.1038/ncomms13342 Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases. Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ disease using EO-1 hyperion hyperspectral imagery. Int J Remote Sens 25:489–498. https://doi.org/10.1080/01431160310001618031 Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008 Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23(5):451–466. https://doi.org/10.1016/j.tplants.2018.02.001 Asaari MSM, Mertens S, Dhondt S et al (2019) Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Comput Electron Agric 162:749–758. https://doi.org/10.1016/j.compag.2019.05.018 Ashourloo D, Mobasheri M, Huete A (2014) Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sens (basel) 6:5107–5123. https://doi.org/10.3390/rs6065107 Ataş M, Yardimci Y, Temizel A (2012) A new approach to aflatoxin detection in chili pepper by machine vision. Comput Electron Agric 87:129–141. https://doi.org/10.1016/j.compag.2012.06.001 Atefi A, Ge Y, Pitla S, Schnable J (2019) In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse. Comput Electron Agric 163:104854. https://doi.org/10.1016/j.compag.2019.104854 Atieno J, Li Y, Langridge P et al (2017) Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping. Sci Rep 7:1300. https://doi.org/10.1038/s41598-017-01211-7 Awlia M, Nigro A, Fajkus J et al (2016) High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Front Plant Sci 7:1414. https://doi.org/10.3389/fpls.2016.01414 Babaeian E, Sadeghi M, Jones SB et al (2019) Ground, proximal and satellite remote sensing of soil moisture. Rev Geophys. https://doi.org/10.1029/2018RG000618 Bai G, Ge Y, Scoby D et al (2019) NU-Spidercam: a large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research. Comput Electron Agric 160:71–81. https://doi.org/10.1016/j.compag.2019.03.009 Baluja J, Diago MP, Balda P et al (2012) Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig Sci 30:511–522. https://doi.org/10.1007/s00271-012-0382-9 Bannari A, Mohamed AMA, El-Battay A (2017) Water stress detection as an indicator of red palm weevil attack using worldview-3 data. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, p 4000–4003 Baranowski P, Jedryczka M, Mazurek W et al (2015) Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One 10:e0122913. https://doi.org/10.1371/journal.pone.0122913 Barbedo JGA (2019) A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 3:40. https://doi.org/10.3390/drones3020040 Bauriegel E, Giebel A, Geyer M et al (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput Electron Agric 75:304–312. https://doi.org/10.1016/j.compag.2010.12.006 Beauchêne K, Leroy F, Fournier A et al (2019) Management and characterization of abiotic stress via PhénoField®, a high-throughput field phenotyping platform. Front Plant Sci 10:904. https://doi.org/10.3389/fpls.2019.00904 Behmann J, Schmitter P, Steinrücken J, Plümer L (2014) Ordinal classification for efficient plant stress prediction in hyperspectral data. Int Arch Photogramm Remote Sens Spatial Inf Sci XL–7:29–36. https://doi.org/10.5194/isprsarchives-XL-7-29-2014 Berdugo CA, Zito R, Paulus S, Mahlein AK (2014) Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathol 63(6):1344–1356. https://doi.org/10.1111/ppa.12219 Bergsträsser S, Fanourakis D, Schmittgen S et al (2015) HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11:1. https://doi.org/10.1186/s13007-015-0043-0 Bhattarai GP, Schmid RB, McCornack BP (2019) Remote sensing data to detect hessian fly infestation in commercial wheat fields. Sci Rep 9:6109. https://doi.org/10.1038/s41598-019-42620-0 Bian J, Zhang Z, Chen J et al (2019) Simplified evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery. Remote Sens (basel) 11:267. https://doi.org/10.3390/rs11030267 Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KF, Lange M, Scholz U, Usadel B (2019) Computational aspects underlying genome to phenome analysis in plants. Plant J 97(1):182–198. https://doi.org/10.1111/tpj.14179 Boureau T (2020) PHENOTIC platform. Portail Data INRAE. https://doi.org/10.15454/u2bwfj Brichet N, Fournier C, Turc O et al (2017) A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods 13:96. https://doi.org/10.1186/s13007-017-0246-7 Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76:156–172. https://doi.org/10.1016/S0034-4257(00)00197-8 Burgos-Artizzu XP, Ribeiro A, Guijarro M, Pajares G (2011) Real-time image processing for crop/weed discrimination in maize fields. Comput Electron Agric 75:337–346. https://doi.org/10.1016/j.compag.2010.12.011 Bürling K, Hunsche M, Noga G (2011) Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. J Plant Physiol 168:1641–1648. https://doi.org/10.1016/j.jplph.2011.03.016 Busemeyer L, Mentrup D, Möller K et al (2013a) BreedVision–a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830–2847. https://doi.org/10.3390/s130302830 Busemeyer L, Ruckelshausen A, Möller K et al (2013b) Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation. Sci Rep 3:2442. https://doi.org/10.1038/srep02442 Cabrera-Bosquet L, Sánchez C, Rosales A et al (2011) Near-infrared reflectance spectroscopy (NIRS) assessment of δ(18)O and nitrogen and ash contents for improved yield potential and drought adaptation in maize. J Agric Food Chem 59:467–474. https://doi.org/10.1021/jf103395z Cabrera-Bosquet L, Fournier C, Brichet N et al (2016) High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol 212:269–281. https://doi.org/10.1111/nph.14027 Calderón R, Navas-Cortés J, Zarco-Tejada P (2015) Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sens (basel) 7:5584–5610. https://doi.org/10.3390/rs70505584 Camino C, González-Dugo V, Hernández P et al (2018) Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. Int J Appl Earth Obs Geoinf 70:105–117. https://doi.org/10.1016/j.jag.2018.04.013 Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H (2015) Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice. Plant Physiol 168(4):1476–1489. https://doi.org/10.1104/pp.15.00450 Cao X, Luo Y, Zhou Y et al (2013) Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance. Crop Prot 45:124–131. https://doi.org/10.1016/j.cropro.2012.12.002 Cârlan I, Haase D, Große-Stoltenberg A, Sandric I (2020) Mapping heat and traffic stress of urban park vegetation based on satellite imagery—a comparison of Bucharest, Romania and Leipzig, Germany. Urban Ecosyst 23:363–377. https://doi.org/10.1007/s11252-019-00916-z Casanova JJ, O’Shaughnessy SA, Evett SR, Rush CM (2014) Development of a wireless computer vision instrument to detect biotic stress in wheat. Sensors 14:17753–17769. https://doi.org/10.3390/s140917753 Chen Z, Wang J, Wang T et al (2021) Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform. Comput Electron Agric 183:105996. https://doi.org/10.1016/j.compag.2021.105996 Chéné Y, Rousseau D, Lucidarme P et al (2012) On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric 82:122–127. https://doi.org/10.1016/j.compag.2011.12.007 Chi G, Huang B, Shi Y et al (2016) Detecting ozone effects in four wheat cultivars using hyperspectral measurements under fully open-air field conditions. Remote Sens Environ 184:329–336. https://doi.org/10.1016/j.rse.2016.07.020 Cho M, Hegde C (2019) Reducing the search space for hyperparameter optimization using group sparsity. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, p 3627–3631 Clark RT, MacCurdy RB, Jung JK et al (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol 156:455–465. https://doi.org/10.1104/pp.110.169102 Coulibaly S, Kamsu-Foguem B, Kamissoko D, Traore D (2019) Deep neural networks with transfer learning in millet crop images. Comput Ind 108:115–120. https://doi.org/10.1016/j.compind.2019.02.003 Crain JL, Wei Y, Barker J et al (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56:965–975. https://doi.org/10.2135/cropsci2015.05.0290 Crain J, Mondal S, Rutkoski J et al (2018) Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. Plant Genome. https://doi.org/10.3835/plantgenome2017.05.0043 Czedik-Eysenberg A, Seitner S, Güldener U et al (2018) The “PhenoBox”, a flexible, automated, open-source plant phenotyping solution. New Phytol 219:808–823. https://doi.org/10.1111/nph.15129 Dachoupakan Sirisomboon C, Putthang R, Sirisomboon P (2013) Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control 33:207–214. https://doi.org/10.1016/j.foodcont.2013.02.034 Dammer K-H, Möller B, Rodemann B, Heppner D (2011) Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Prot 30:420–428. https://doi.org/10.1016/j.cropro.2010.12.015 Dangwal N, Patel NR, Kumari M, Saha SK (2016) Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM. Geocarto Int 31:682–693. https://doi.org/10.1080/10106049.2015.1073369 Das PK, Laxman B, Rao SVCK et al (2015) Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India. Int J Pest Manag 61:359–368. https://doi.org/10.1080/09670874.2015.1072652 Das R, Banerjee M, De S (eds) (2019) Emerging trends in disruptive technology management for sustainable development, illustrated edition. CRC Press, Boca Raton Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. Int J Remote Sens 25:5403–5413. https://doi.org/10.1080/0143116042000274015 DeChant C, Wiesner-Hanks T, Chen S et al (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107:1426–1432. https://doi.org/10.1094/PHYTO-11-16-0417-R Del Fiore A, Reverberi M, Ricelli A et al (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int J Food Microbiol 144:64–71. https://doi.org/10.1016/j.ijfoodmicro.2010.08.001 Delalieux S, van Aardt J, Keulemans W et al (2007) Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications. Eur J Agron 27:130–143. https://doi.org/10.1016/j.eja.2007.02.005 Devadas R, Lamb DW, Backhouse D, Simpfendorfer S (2015) Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precis Agric 16(5):477–491. https://doi.org/10.1007/s11119-015-9390-0 Diezma B, Lleó L, Roger JM et al (2013) Examination of the quality of spinach leaves using hyperspectral imaging. Postharvest Biol Technol 85:8–17. https://doi.org/10.1016/j.postharvbio.2013.04.017 Dobbels AA, Lorenz AJ (2019) Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system. Plant Methods 15:97. https://doi.org/10.1186/s13007-019-0478-9 Dong T, Liu J, Qian B et al (2016) Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. Int J Appl Earth Obs Geoinf 49:63–74. https://doi.org/10.1016/j.jag.2016.02.001 Ehsani R, Maja JM (2013) The rise of small UAVs in precision agriculture. Resour Mag 20:18–19 Eichner J, Zeller G, Laubinger S, Rätsch G (2011) Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays. BMC Bioinformatics 12:55. https://doi.org/10.1186/1471-2105-12-55 ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74 Fabre J, Dauzat M, Nègre V, Wuyts N, Tireau A, Gennari E, Neveu P, Tisné S, Massonnet C, Hummel I, Granier C (2011) PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 11(1):1–7 Fieuzal R, Bustillo V, Collado D, Dedieu G (2020) Combined use of multi-temporal landsat-8 and sentinel-2 images for wheat yield estimates at the intra-plot spatial scale. Agronomy 10:327. https://doi.org/10.3390/agronomy10030327 Flood PJ, Kruijer W, Schnabel SK et al (2016) Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. Plant Methods 12:14. https://doi.org/10.1186/s13007-016-0113-y Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors. https://doi.org/10.3390/s17092022 Fukatsu T, Watanabe T, Hu H et al (2012) Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants, field servers, and image analysis. Comput Electron Agric 80:8–16. https://doi.org/10.1016/j.compag.2011.10.005 Garcia-Ruiz F, Sankaran S, Maja JM et al (2013) Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput Electron Agric 91:106–115. https://doi.org/10.1016/j.compag.2012.12.002 Garriga M, Retamales JB, Romero-Bravo S, Caligari PD, Lobos GA (2014) Chlorophyll, anthocyanin, and gas exchange changes assessed by spectroradiometry in Fragaria chiloensis under salt stress. J Integr Plant Biol 56(5):505–515. https://doi.org/10.1111/jipb.12193 Gehan MA, Kellogg EA (2017) High-throughput phenotyping. Am J Bot 104:505–508. https://doi.org/10.3732/ajb.1700044 Gennaro SD, Battiston E, Marco SD et al (2016) Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathol Mediterr 55:262–275 Gitelson A, Merzlyak MN (1994) Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol Biol 22:247–252. https://doi.org/10.1016/1011-1344(93)06963-4 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. https://doi.org/10.1016/S0034-4257(96)00072-7 Gitelson AA, Kaufman YJ, Stark R, Rundquist D (2002) Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ 80:76–87. https://doi.org/10.1016/S0034-4257(01)00289-9 Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282. https://doi.org/10.1078/0176-1617-00887 Gosseau F, Blanchet N, Varès D et al (2018) Heliaphen, an outdoor high-throughput phenotyping platform for genetic studies and crop modeling. Front Plant Sci 9:1908. https://doi.org/10.3389/fpls.2018.01908 Granier C, Aguirrezabal L, Chenu K et al (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169:623–635. https://doi.org/10.1111/j.1469-8137.2005.01609.x Gregor K, LeCun Y (2010) Learning fast approximations of sparse coding. In: Proceedings of the 27th international conference on international conference on machine learning, p 399–406 Guerrero JM, Pajares G, Montalvo M et al (2012) Support vector machines for crop/weeds identification in maize fields. Expert Syst Appl 39:11149–11155. https://doi.org/10.1016/j.eswa.2012.03.040 Gulli A, Pal S (2017) Deep learning with Keras: implementing deep learning models and neural networks with the power of Python. Packt Publishing, Birmingham Haagsma M, Page G, Johnson J, Still C, Waring K, Sniezko R, Selker J, (2020). Is more data better? A comparison of multi-and hyperspectral imaging in phenotyping. In: EGU General Assembly Conference Abstracts, p 10673 Haboudane D, Tremblay N, Miller JR, Vigneault P (2008) Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans Geosci Remote Sensing 46:423–437. https://doi.org/10.1109/TGRS.2007.904836 Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice (NY) 7:16. https://doi.org/10.1186/s12284-014-0016-3 Hallau L, Neumann M, Klatt B et al (2017) Automated identification of sugar beet diseases using smartphones. Plant Pathol 67:399–410. https://doi.org/10.1111/ppa.12741 Hassan MA, Yang M, Rasheed A et al (2019) A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci 282:95–103. https://doi.org/10.1016/j.plantsci.2018.10.022 He L, Mostovoy G (2019) Cotton yield estimate using sentinel-2 data and an ecosystem model over the southern US. Remote Sens (basel) 11:2000. https://doi.org/10.3390/rs11172000 Hernández-Rabadán DL, Ramos-Quintana F, Guerrero Juk J (2014) Integrating SOMs and a Bayesian classifier for segmenting diseased plants in uncontrolled environments. Sci World J 2014:214674. https://doi.org/10.1155/2014/214674 Hillnhütter C, Mahlein AK, Sikora RA, Oerke EC (2011) Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Res 122:70–77. https://doi.org/10.1016/j.fcr.2011.02.007 Huete A, Didan K, Miura T et al (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213. https://doi.org/10.1016/S0034-4257(02)00096-2 Hunt ER, Daughtry CST, Eitel JUH, Long DS (2011) Remote sensing leaf chlorophyll content using a visible band index. Agron J 103:1090. https://doi.org/10.2134/agronj2010.0395 Hunt ER, Doraiswamy PC, McMurtrey JE et al (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Obs Geoinf 21:103–112. https://doi.org/10.1016/j.jag.2012.07.020 Hunter MC, Smith RG, Schipanski ME et al (2017) Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67:386–391. https://doi.org/10.1093/biosci/bix010 Jackson RD, Huete AR (1991) Interpreting vegetation indices. Prev Vet Med 11:185–200. https://doi.org/10.1016/S0167-5877(05)80004-2 Jansen M, Gilmer F, Biskup B et al (2009) Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 36:902–914. https://doi.org/10.1071/FP09095 Jeudy C, Adrian M, Baussard C et al (2016) RhizoTubes as a new tool for high throughput imaging of plant root development and architecture: test, comparison with pot grown plants and validation. Plant Methods 12:31. https://doi.org/10.1186/s13007-016-0131-9 Jones CD, Jones JB, Lee WS (2010) Diagnosis of bacterial spot of tomato using spectral signatures. Comput Electron Agric 74:329–335. https://doi.org/10.1016/j.compag.2010.09.008 Kandpal LM, Lee S, Kim MS et al (2015) Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51:171–176. https://doi.org/10.1016/j.foodcont.2014.11.020 Kaur B, Sandhu KS, Kamal R et al (2021) Omics for the improvement of abiotic, biotic, and agronomic traits in major cereal crops: applications, challenges, and prospects. Plants 10:1989. https://doi.org/10.3390/PLANTS10101989 Kersting K, Xu Z, Wahabzada M, et al (2012) Pre-symptomatic prediction of plant drought stress using dirichlet-aggregation regression on hyperspectral images Kim JY (2020) Roadmap to high throughput phenotyping for plant breeding. J Biosyst Eng 45(1):43–55 Kim SW, Kim HJ, Kim JH et al (2011) A rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by direct analysis in real-time mass spectrometry. Plant Methods 7:14. https://doi.org/10.1186/1746-4811-7-14 Kim SL, Kim N, Lee H et al (2020) High-throughput phenotyping platform for analyzing drought tolerance in rice. Planta 252:38. https://doi.org/10.1007/s00425-020-03436-9 Kloth KJ, Ten Broeke CJ, Thoen MP et al (2015) High-throughput phenotyping of plant resistance to aphids by automated video tracking. Plant Methods 11:4. https://doi.org/10.1186/s13007-015-0044-z Kokhan S, Vostokov A (2020) Using vegetative indices to quantify agricultural crop characteristics. J Ecol Eng 21:120–127. https://doi.org/10.12911/22998993/119808 Kong W, Liu F, Zhang C et al (2014) Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging. Spectrochim Acta A Mol Biomol Spectrosc 118:498–502. https://doi.org/10.1016/j.saa.2013.09.009 Krajewski P, Chen D, Ćwiek H, van Dijk AD, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap JP, van Oeveren J (2015) Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 66(18):5417–5427 Le Marié C, Kirchgessner N, Flütsch P et al (2016) RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion. Plant Methods 12:40. https://doi.org/10.1186/s13007-016-0140-8 LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539 Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111. https://doi.org/10.3390/s141120078 Li H, Wang C, Wang X, Hou P, Luo B, Song P, Pan D, Li A, Chen L (2019) Disposable stainless steel-based electrochemical microsensor for in vivo determination of indole-3-acetic acid in soybean seedlings. Biosens Bioelectron 126:193–199 Liebisch F, Kirchgessner N, Schneider D et al (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:9. https://doi.org/10.1186/s13007-015-0048-8 Lin K, Gong L, Huang Y et al (2019) Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front Plant Sci 10:155. https://doi.org/10.3389/fpls.2019.00155 Liu M, Wang T, Skidmore AK, Liu X (2018) Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images. Sci Total Environ 637–638:18–29. https://doi.org/10.1016/j.scitotenv.2018.04.415 López-López M, Calderón R, González-Dugo V, Zarco-Tejada PJ, Fereres E (2016) Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sensing 8(4):276. https://doi.org/10.3390/rs8040276 Louhaichi M, Borman MM, Johnson DE (2001) Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int 16:65–70. https://doi.org/10.1080/10106040108542184 Lu Y, Yi S, Zeng N et al (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384. https://doi.org/10.1016/j.neucom.2017.06.023 Mahajan GR, Sahoo RN, Pandey RN et al (2014) Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precis Agric 15:499–522. https://doi.org/10.1007/s11119-014-9348-7 Mahlein A-K, Oerke E-C, Steiner U, Dehne H-W (2012a) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197–209. https://doi.org/10.1007/s10658-011-9878-z Mahlein A-K, Steiner U, Hillnhütter C et al (2012b) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8:3. https://doi.org/10.1186/1746-4811-8-3 Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A.H (2011) Big data: the next frontier for innovation, competition, and productivity. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation Maresma Á, Ariza M, Martínez E et al (2016) Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sens (basel) 8:973. https://doi.org/10.3390/rs8120973 Martins SM, de Brito GG, da Gonçalves WC et al (2020) PhenoRoots: an inexpensive non-invasive phenotyping system to assess the variability of the root system architecture. Sci Agric. https://doi.org/10.1590/1678-992x-2018-0420 Mathieu L, Lobet G, Tocquin P, Périlleux C (2015) “Rhizoponics”: a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants. Plant Methods 11:3. https://doi.org/10.1186/s13007-015-0046-x Meacham-Hensold K, Fu P, Wu J, Serbin S, Montes CM, Ainsworth E, Guan K, Dracup E, Pederson T, Driever S, Bernacchi C (2020) Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. J Exp Bot 71(7):2312–2328 Mer CR, Wahabzada M, Ballvora A et al (2012) Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Funct Plant Biol 39:878–890. https://doi.org/10.1071/FP12060 Mewes T, Franke J, Menz G (2011) Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agric 12(6):795–812. https://doi.org/10.1007/s11119-011-9222-9 Min M, Lee WS, Burks TF, Jordan JD, Schumann AW, Schueller JK, Xie H (2008) Design of a hyperspectral nitrogen sensing system for orange leaves. Comput Electron Agric 63(2):215–226 Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinformatics 18:851–869. https://doi.org/10.1093/bib/bbw068 Mir RR, Reynolds M, Pinto F et al (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282:60–72. https://doi.org/10.1016/j.plantsci.2019.01.007 Mishra P, Polder G, Vilfan N (2020) Close range spectral imaging for disease detection in plants using autonomous platforms: a review on recent studies. Curr Robot Rep 1:43–48. https://doi.org/10.1007/s43154-020-00004-7 Mo C, Kim MS, Kim G et al (2015) Detecting drought stress in soybean plants using hyperspectral fluorescence imaging. J Biosyst Eng 40:335–344. https://doi.org/10.5307/JBE.2015.40.4.335 Mokhtar U, Ali MAS, Hassanien AE, Hefny H (2015) Identifying two of tomatoes leaf viruses using support vector machine. In: Mandal JK, Satapathy SC, Kumar Sanyal M et al (eds) Information systems design and intelligent applications. Springer India, New Delhi, pp 771–782 Nagasubramanian K, Jones S, Singh AK et al (2019) Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15:98. https://doi.org/10.1186/s13007-019-0479-8 Nagel KA, Putz A, Gilmer F et al (2012) GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct Plant Biol 39:891. https://doi.org/10.1071/FP12023 Naik HS, Zhang J, Lofquist A et al (2017) A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23. https://doi.org/10.1186/s13007-017-0173-7 Navrozidis I, Alexandridis TK, Dimitrakos A et al (2018) Identification of purple spot disease on asparagus crops across spatial and spectral scales. Comput Electron Agric 148:322–329. https://doi.org/10.1016/j.compag.2018.03.035 Ochoa D, Cevallos J, Vargas G et al (2016) Hyperspectral imaging system for disease scanning on banana plants. In: Kim MS, Chao K, Chin BA (eds) Sensing for agriculture and food quality and safety VIII. SPIE, Bellingham, p 98640M Oerke E-C, Mahlein A-K, Steiner U (2014) Proximal sensing of plant diseases. In: Gullino ML, Bonants PJM (eds) Detection and diagnostics of plant pathogens. Springer Netherlands, Dordrecht, pp 55–68 Omran E-S (2018) Remote estimation of vegetation parameters using narrowband sensor for precision agriculture in arid environment. Egypt J Soil Sci 58:73–92. https://doi.org/10.21608/ejss.2018.5614 Onoyama H, Ryu C, Suguri M, Iida M (2013) Potential of hyperspectral imaging for constructing a year-invariant model to estimate the nitrogen content of rice plants at the panicle initiation stage. IFAC Proc Vol 46:219–224. https://doi.org/10.3182/20130828-2-SF-3019.00054 Peña JM, Torres-Sánchez J, Serrano-Pérez A et al (2015) Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15:5609–5626. https://doi.org/10.3390/s150305609 Pérez AJ, López F, Benlloch JV, Christensen S (2000) Colour and shape analysis techniques for weed detection in cereal fields. Comput Electron Agric 25:197–212. https://doi.org/10.1016/S0168-1699(99)00068-X Pérez-Bueno ML, Pineda M, Cabeza FM, Barón M (2016) Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Front Plant Sci 7:1790. https://doi.org/10.3389/fpls.2016.01790 Pérez-Enciso M, Zingaretti LM (2019) A guide for using deep learning for complex trait genomic prediction. Genes (basel). https://doi.org/10.3390/genes10070553 Pérez-Ruiz M, Prior A, Martinez-Guanter J et al (2020) Development and evaluation of a self-propelled electric platform for high-throughput field phenotyping in wheat breeding trials. Comput Electron Agric 169:105237. https://doi.org/10.1016/j.compag.2020.105237 Peri S (2020) PhytoOracle: a scalable, modular framework for phenomics data processing and trait extraction. In: Plant and Animal Genome XXVIII Conference (January 11–15, 2020) Pessarakli M (2019) Handbook of plant and crop stress, 4th edn. CRC Press, Taylor & Francis Group, Boca Raton, FL Pettorelli N (2019) Satellite remote sensing and the management of natural resources. Oxford University Press, Oxford Pineda M, Barón M, Pérez-Bueno M-L (2020) Thermal imaging for plant stress detection and phenotyping. Remote Sens (basel) 13:68. https://doi.org/10.3390/rs13010068 Prabhakar M, Prasad YG, Desai S et al (2013a) Hyperspectral remote sensing of yellow mosaic severity and associated pigment losses in Vigna mungo using multinomial logistic regression models. Crop Prot 45:132–140. https://doi.org/10.1016/j.cropro.2012.12.003 Prabhakar M, Prasad YG, Vennila S et al (2013b) Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton. Comput Electron Agric 97:61–70. https://doi.org/10.1016/j.compag.2013.07.004 Prashar A, Jones HG (2016) Assessing drought responses using thermal infrared imaging. Methods Mol Biol 1398:209–219. https://doi.org/10.1007/978-1-4939-3356-3_17 Qiu Q, Sun N, Bai H et al (2019) Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a “Phenomobile.” Front Plant Sci 10:554. https://doi.org/10.3389/fpls.2019.00554 Ramcharan A, Baranowski K, McCloskey P et al (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852. https://doi.org/10.3389/fpls.2017.01852 Ramcharan A, McCloskey P, Baranowski K et al (2019) A mobile-based deep learning model for cassava disease diagnosis. Front Plant Sci 10:272. https://doi.org/10.3389/fpls.2019.00272 Ranzato MA, Mnih V, Susskind JM, Hinton GE (2013) Modeling natural images using gated MRFs. IEEE Trans Pattern Anal Mach Intell 35(9):2206–2222 Raza S-A, Smith HK, Clarkson GJJ et al (2014) Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE 9:e97612. https://doi.org/10.1371/journal.pone.0097612 Raza S-A, Prince G, Clarkson JP, Rajpoot NM (2015) Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One 10:e0123262. https://doi.org/10.1371/journal.pone.0123262 Rey B, Aleixos N, Cubero S, Blasco J (2019) Xf-Rovim. A field robot to detect olive trees infected by Xylella fastidiosa using proximal sensing. Remote Sens (basel) 11:221. https://doi.org/10.3390/rs11030221 Reynolds D, Ball J, Bauer A, Davey R, Griffiths S, Zhou J (2019) CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience 8(3):giz009. https://doi.org/10.1093/gigascience/giz009 Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES (2019) New sensors and data-driven approaches—a path to next generation phenomics. Plant Sci 282:2–10. https://doi.org/10.1016/j.plantsci.2019.01.011 Rousseau C, Belin E, Bove E et al (2013) High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9:17. https://doi.org/10.1186/1746-4811-9-17 Royimani L, Mutanga O, Odindi J et al (2018) Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs). Phys Chem Earth, Parts a/b/c. https://doi.org/10.1016/j.pce.2018.12.004 Sadok W, Naudin P, Boussuge B et al (2007) Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions. Plant Cell Environ 30:135–146. https://doi.org/10.1111/j.1365-3040.2006.01611.x Sagan V, Maimaitijiang M, Sidike P et al (2019) Uav/satellite multiscale data fusion for crop monitoring and early stress detection. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-2/W13:715–722. https://doi.org/10.5194/isprs-archives-XLII-2-W13-715-2019 Saini DK, Chopra Y, Singh J et al (2022) Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breed 42:1. https://doi.org/10.1007/s11032-021-01272-7 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. https://doi.org/10.1104/pp.17.00707 Salgadoe A, Robson A, Lamb D et al (2018) Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis. Remote Sens (basel) 10:226. https://doi.org/10.3390/rs10020226 Samantara K, Shiv A, de Sousa LL, Sandhu KS, Priyadarshini P, Mohapatra SR (2021) A comprehensive review on epigenetic mechanisms and application of epigenetic modifications for crop improvement. Environ Exp Bot 188:104479 Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3:210–229. https://doi.org/10.1147/rd.33.0210 Sandhu KS, Lozada DN, Zhang Z et al (2020) Deep learning for predicting complex traits in spring wheat breeding program. Front Plant Sci 11:613325. https://doi.org/10.3389/fpls.2020.613325 Sandhu KS, Mihalyov PD, Lewien MJ et al (2021a) Combining genomic and phenomic information for predicting grain protein content and grain yield in spring wheat. Front Plant Sci 12:613300. https://doi.org/10.3389/fpls.2021.613300 Sandhu KS, Patil SS, Pumphrey MO, Carter AH (2021b) Multi-trait machine and deep learning models for genomic selection using spectral information in a wheat breeding program. The Plant Genome. https://doi.org/10.1002/TPG2.20119 Sandhu KS, Mihalyov PD, Lewien MJ, Pumphrey MO, Carter AH (2021bd) Genomic selection and genome-wide association studies for grain protein content stability in a nested association mapping population of wheat. Agronomy 11(12):2528 Sandhu KS, Aoun M, Morris CF, Carter AH (2021c) Genomic selection for end-use quality and processing traits in soft white winter wheat breeding program with machine and deep learning models. Biol 10:689. https://doi.org/10.3390/BIOLOGY10070689 Sandhu KS, Merrick L, Lewien MJ et al (2022a) Prospectus of genomic selection and phenomics in cereal, legume and oilseed breeding programs. Front Genet 12:829131. https://doi.org/10.3389/fgene.2021.829131 Sandhu KS, Patil SS, Aoun M, Carter AH (2022b) Multi-trait multi-environment genomic prediction for end-use quality traits in winter wheat. Front Gen 13:831020. https://doi.org/10.3389/fgene.2022.831020 Sanga S, Mero V, Machuve D, Mwanganda D (2020) Mobile-based deep learning models for banana diseases detection. Eng Technol Appl Sci Res 10:5674 Sankaran S, Mishra A, Maja JM, Ehsani R (2011) Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Comput Electron Agric 77:127–134. https://doi.org/10.1016/j.compag.2011.03.004 Sankaran S, Quirós JJ, Miklas PN (2019) Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean. Comput Electron Agric 165:104965. https://doi.org/10.1016/j.compag.2019.104965 Schikora M, Neupane B, Madhogaria S et al (2012) An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium. BMC Bioinformatics 13:171. https://doi.org/10.1186/1471-2105-13-171 Segarra J, Buchaillot ML, Araus JL, Kefauver SC (2020) Remote sensing for precision agriculture: sentinel-2 improved features and applications. Agronomy 10:641. https://doi.org/10.3390/agronomy10050641 Shahin MA, Symons SJ (2011) Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Comput Electron Agric 75:107–112. https://doi.org/10.1016/j.compag.2010.10.004 Shibayama M, Sakamoto T, Takada E et al (2009) Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital cameras. Plant Prod Sci 12:293–306. https://doi.org/10.1626/pps.12.293 Singh AK, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci 23(10):883–898. https://doi.org/10.1016/j.tplants.2018.07.004 Sirault XRR, James RA, Furbank RT (2009) A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Functional Plant Biol 36:970. https://doi.org/10.1071/FP09182 Sishodia RP, Ray RL, Singh SK (2020) Applications of remote sensing in precision agriculture: a review. Remote Sensing 12(19):3136. https://doi.org/10.3390/rs12193136 Song X, Yang C, Wu M et al (2017) Evaluation of sentinel-2A satellite imagery for mapping cotton root rot. Remote Sens (basel) 9:906. https://doi.org/10.3390/rs9090906 Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83–116. https://doi.org/10.1146/annurev.phyto.43.113004.133839 Teke M, Deveci HS, Haliloglu O, et al (2013) A short survey of hyperspectral remote sensing applications in agriculture. In: 2013 6th International Conference on Recent Advances in Space Technologies (RAST). IEEE, p 171–176 Tisné S, Serrand Y, Bach L et al (2013) Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J 74:534–544. https://doi.org/10.1111/tpj.12131 Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150. https://doi.org/10.1016/0034-4257(79)90013-0 Vigneau N, Ecarnot M, Rabatel G, Roumet P (2011) Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Res 122:25–31. https://doi.org/10.1016/j.fcr.2011.02.003 Vincini M, Frazzi E, D’Alessio P (2008) A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis Agric 9:303–319. https://doi.org/10.1007/s11119-008-9075-z 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. https://doi.org/10.1071/FP16163 Vougioukas SG (2019) Agricultural robotics. Annu Rev Control Robot Auton Syst 2:365–392. https://doi.org/10.1146/annurev-control-053018-023617 Wahabzada M, Mahlein A-K, Bauckhage C et al (2015) Metro maps of plant disease dynamics–automated mining of differences using hyperspectral images. PLoS One 10:e0116902. https://doi.org/10.1371/journal.pone.0116902 Wang J, Nakano K, Ohashi S (2011) Nondestructive detection of internal insect infestation in jujubes using visible and near-infrared spectroscopy. Postharvest Biol Technol 59:272–279. https://doi.org/10.1016/j.postharvbio.2010.09.017 Wang W, Heitschmidt GW, Ni X et al (2014) Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging. Food Control 42:78–86. https://doi.org/10.1016/j.foodcont.2014.01.038 Wang X, Zhao C, Guo N et al (2015) Determining the canopy water stress for spring wheat using canopy hyperspectral reflectance data in loess plateau semiarid regions. Spectrosc Lett 48:492–498. https://doi.org/10.1080/00387010.2014.909495 Wetterich CB, de Oliveira F, Neves R, Belasque J et al (2017) Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods. Appl Opt 56:15. https://doi.org/10.1364/AO.56.000015 Wiesner-Hanks T, Stewart EL, Kaczmar N, DeChant C, Wu H, Nelson RJ, Lipson H, Gore MA (2018) Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes 11(1):1–3. https://doi.org/10.1186/s13104-018-3548-6 Wilkinson MD, Dumontier M, Aalbersberg IJJ et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18 Wu S, Wen W, Wang Y et al (2020) MVS-pheno: a portable and low-cost phenotyping platform for maize shoots using multiview stereo 3D reconstruction. Plant Phenomics 2020:1848437. https://doi.org/10.34133/2020/1848437 Xie C, Shao Y, Li X, He Y (2015) Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Sci Rep 5:16564. https://doi.org/10.1038/srep16564 Xu Y (2016) Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129(4):653–673 Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sensors 2017:1–17. https://doi.org/10.1155/2017/1353691 Yang W, Guo Z, Huang C et al (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087. https://doi.org/10.1038/ncomms6087 Yang W, Feng H, Zhang X et al (2020) Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant 13:187–214. https://doi.org/10.1016/j.molp.2020.01.008 Yao H, Hruska Z, Kincaid R et al (2013) Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosys Eng 115:125–135. https://doi.org/10.1016/j.biosystemseng.2013.03.006 Yazdanbakhsh N, Fisahn J (2009) High throughput phenotyping of root growth dynamics, lateral root formation, root architecture and root hair development enabled by PlaRoM. Funct Plant Biol 36:938–946. https://doi.org/10.1071/FP09167 Yeh Y-H, Chung W-C, Liao J-Y et al (2016) Strawberry foliar anthracnose assessment by hyperspectral imaging. Comput Electron Agric 122:1–9. https://doi.org/10.1016/j.compag.2016.01.012 Young SN, Kayacan E, Peschel JM (2018) Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precis Agric. https://doi.org/10.1007/s11119-018-9601-6 Yu K-Q, Zhao Y-R, Li X-L et al (2014) Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PLoS One 9:e116205. https://doi.org/10.1371/journal.pone.0116205 Yuan L, Pu R, Zhang J et al (2016) Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precision Agric 17:332–348. https://doi.org/10.1007/s11119-015-9421-x Yue J, Yang G, Li C et al (2017) Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens (basel) 9:708. https://doi.org/10.3390/rs9070708 Zaman-Allah M, Vergara O, Araus JL et al (2015) Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11:35. https://doi.org/10.1186/s13007-015-0078-2 Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012) Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 117:322–337. https://doi.org/10.1016/j.rse.2011.10.007 Zhang J, Pu R, Huang W et al (2012a) Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Res 134:165–174. https://doi.org/10.1016/j.fcr.2012.05.011 Zhang J-C, Pu R, Wang J et al (2012b) Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Comput Electron Agric 85:13–23. https://doi.org/10.1016/j.compag.2012.03.006 Zhang C, Liu F, Kong W, He Y (2015) Application of visible and near-infrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves. Sensors 15:16576–16588. https://doi.org/10.3390/s150716576 Zhang C, Pumphrey MO, Zhou J et al (2019a) Development of an automated high- throughput phenotyping system for wheat evaluation in a controlled environment. Trans ASABE 62:61–74. https://doi.org/10.13031/trans.12856 Zhang L, Zhang H, Niu Y, Han W (2019b) Mapping maize water stress based on UAV multispectral remote sensing. Remote Sens (basel) 11:605. https://doi.org/10.3390/rs11060605 Zheng Q, Huang W, Cui X et al (2018) New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors. https://doi.org/10.3390/s18030868 Zhou J, Reynolds D, Websdale D et al (2017) CropQuant: an automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture. BioRxiv. https://doi.org/10.1101/161547 Zhu F, Saluja M, Dharni JS et al (2021) PhenoImage : An open-source graphical user interface for plant image analysis. Plant Phenome J. https://doi.org/10.1002/ppj2.20015