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Research Article | Open Access

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

Libin Wu1,2Han Shao1,3Jiayi Li1,2Chen Chen1,2Nana Hu1,3Biyun Yang1,2Haiyong Weng1,2Lirong Xiang4( )Dapeng Ye1,2( )
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
Center for Artificial Intelligence in Agriculture, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27606, USA
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Abstract

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

References

1

Lichtenthaler HK. The stress concept in plants: An introduction. Ann N Y Acad Sci. 1998;851:187–198.

2
Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis M. Climate change 2021: The physical science basis. Contribution of Working Group Ⅰ to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 2021.
3
World Health Organization. The state of food security and nutrition in the world 2021: Transforming food systems for food security, improved nutrition and affordable healthy diets for all. Food and Agriculture Organization of the United Nations; 2021.
4

Rivero RM, Mittler R, Blumwald E, Zandalinas SI. Developing climate-resilient crops: Improving plant tolerance to stress combination. Plant J. 2022;109(2):373–389.

5

Alscher RG, Cumming JR. Stress responses in plants: Adaptation and acclimation mechanisms. Hoboken (NJ): Wiley-Liss; 1990.

6

Mu Q, Guo T, Li X, Yu J. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. New Phytol. 2022;233(4):1768–1779.

7

Al-Tamimi N, Langan P, Bernad V, Walsh J, Mangina E, Negrao S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol. 2022;12(6):Article 210353.

8

Fountas S, Malounas I, Athanasakos L, Avgoustakis I, Espejo-Garcia B. AI-assisted vision for agricultural robots. AgriEngineering. 2022;4(3):674–694.

9

Machwitz M, Pieruschka R, Berger K, Schlerf M, Aasen H, Fahrner S, Jimenez-Berni J, Baret F, Rascher U. Bridging the gap between remote sensing and plant phenotyping-challenges and opportunities for the next generation of sustainable agriculture. Front Plant Sci. 2021;12:749374.

10

Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. Trends Plant Sci. 2022;27(2):191–208.

11

Waiphara P, Bourgenot C, Compton LJ, Prashar A. Optical imaging resources for crop phenotyping and stress detection. Methods Mol Biol. 2022;2494:255–265.

12
Udayakumar N. Visible light imaging. In: Manickavasagan A, Jayasuriya H, editors. Imaging with electromagnetic spectrum: Applications in food and agriculture. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 67–86.
13

Zahir SADM, Omar AF, Jamlos MF, Azmi MAM, Muncan J. A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection. Sens Actuators A Phys. 2022;338:Article 113468.

14

Ryckewaert M, Héran D, Simonneau T, Abdelghafour F, Boulord R, Saurin N, Moura D, Mas-Garcia S, Bendoula R. Physiological variable predictions using VIS–NIR spectroscopy for water stress detection on grapevine: Interest in combining climate data using multiblock method. Comput Electron Agric. 2022;197:Article 106973.

15

Lazarević B, Kontek M, Carović-Stanko K, Clifton-Brown J, Al Hassan M, Trindade LM, Jurišić V. Multispectral image analysis detects differences in drought responses in novel seeded Miscanthus sinensis hybrids. GCB Bioenergy. 2022;14(11):1219–1234.

16

Zhao Y, Zheng B, Chapman SC, Laws K, George-Jaeggli B, Hammer GL, Jordan DR, Potgieter AB. Detecting sorghum plant and head features from multispectral UAV imagery. Plant Phenomics. 2021;2021:9874650.

17

Lassalle G. Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review. Sci Total Environ. 2021;788:Article 147758.

18

Ruett M, Junker-Frohn LV, Siegmann B, Ellenberger J, Jaenicke H, Whitney C, Luedeling E, Tiede-Arlt P, Rascher U. Hyperspectral imaging for high-throughput vitality monitoring in ornamental plant production. Sci Hortic. 2022;291:110546.

19

Das S, Chapman S, Christopher J, Choudhury MR, Menzies NW, Apan A, Dang YP. UAV-thermal imaging: A technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils—A case review on wheat. Remote Sens Appl. 2021;23:100583.

20

Moustakas M, Calatayud Á, Guidi L. Chlorophyll fluorescence imaging analysis in biotic and abiotic stress. Front Plant Sci. 2021;12:658500.

21

Jin X, Zarco-Tejada PJ, Schmidhalter U, Reynolds MP, Hawkesford MJ, Varshney RK, Yang T, Nie C, Li Z, Ming B, et al. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geosci Remote Sens Mag. 2021;9(1):200–231.

22

Piovesan A, Vancauwenberghe V, Van De Looverbosch T, Verboven P, Nicolai B. X-ray computed tomography for 3D plant imaging. Trends Plant Sci. 2021;26(11):1171–1185.

23

Kotwaliwale N, Singh K, Kalne A, Jha SN, Seth N, Kar A. X-ray imaging methods for internal quality evaluation of agricultural produce. J Food Sci Technol. 2014;51(1):1–15.

24

Forero MG, Murcia HF, Mendez D, Betancourt-Lozano J. LiDAR platform for acquisition of 3D plant phenotyping database. Plants. 2022;11(17):2199.

25

Jin SC, Sun XL, Wu FF, Su YJ, Li YM, Song SL, Xu KX, Ma Q, Baret F, Jiang D, et al. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS J Photogramm Remote Sens. 2021;171:202–223.

26

van Dusschoten D, Metzner R, Kochs J, Postma JA, Pflugfelder D, Buhler J, Schurr U, Jahnke S. Quantitative 3D analysis of plant roots growing in soil using magnetic resonance imaging. Plant Physiol. 2016;170(3):1176–1188.

27

Mincke J, Courtyn J, Vanhove C, Vandenberghe S, Steppe K. Guide to plant-PET imaging using 11CO2. Front Plant Sci. 2021;12:602550.

28

Song P, Wang J, Guo X, Yang W, Zhao C. High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. Crop J. 2021;9(3):633–645.

29

De Diego N, Furst T, Humplik JF, Ugena L, Podlesakova K, Spichal L. An automated method for high-throughput screening of Arabidopsis rosette growth in multi-well plates and its validation in stress conditions. Front Plant Sci. 2017;8:1702.

30

Marchetti CF, Ugena L, Humplik JF, Polak M, Cavar Zeljkovic S, Podlesakova K, Furst T, De Diego N, Spichal L. A novel image-based screening method to study water-deficit response and recovery of barley populations using canopy dynamics phenotyping and simple metabolite profiling. Front Plant Sci. 2019;10:1252.

31

Zea M, Souza A, Yang Y, Lee L, Nemali K, Hoagland L. Leveraging high-throughput hyperspectral imaging technology to detect cadmium stress in two leafy green crops and accelerate soil remediation efforts. Environ Pollut. 2022;292:Article 118405.

32

Christenhusz MJ, Byng JW. The number of known plants species in the world and its annual increase. Phytotaxa. 2016;261(3):201–217.

33
Vascular plant. Britannica. 15 Mar 2024. https://www.britannica.com/plant/tracheophyte.
34

Anil Kumar S, Hima Kumari P, Nagaraju M, Sudhakar Reddy P, Durga Dheeraj T, Mack A, Katam R, Kavi Kishor PB. Genome-wide identification and multiple abiotic stress transcript profiling of potassium transport gene homologs in Sorghum bicolor. Front Plant Sci. 2022;13:965530.

35

Li H, Wang Y, Fan K, Mao Y, Shen Y, Ding Z. Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data. Front Plant Sci. 2022;13:898962.

36

Bernhardt JR, O'Connor MI, Sunday JM, Gonzalez A. Life in fluctuating environments. Philos Trans R Soc B. 1814;2020(375):20190454.

37

Bohnert HJ, Nelson DE, Jensen RG. Adaptations to environmental stresses. Plant Cell. 1995;7(7):1099–1111.

38

Malone SR, Ashworth EN. Freezing stress response in woody tissues observed using low-temperature scanning electron microscopy and freeze substitution techniques. Plant Physiol. 1991;95(3):871–881.

39

Taiz L, Zeiger E, Møller IM, Murphy A. Plant physiology and development. Sunderland (MA): Sinauer Associates Incorporated; 2015.

40

Ye D, Wu L, Li X, Atoba TO, Wu W, Weng H. A synthetic review of various dimensions of non-destructive plant stress phenotyping. Plants. 2023;12(8):1698.

41

Shimazaki K-i, Doi M, Assmann SM, Kinoshita T. Light regulation of stomatal movement. Annu Rev Plant Biol. 2007;58(1):219–247.

42

Erickson E, Wakao S, Niyogi KK. Light stress and photoprotection in Chlamydomonas reinhardtii. Plant J. 2015;82(3):449–465.

43

Hutin C, Nussaume L, Moise N, Moya I, Kloppstech K, Havaux M. Early light-induced proteins protect Arabidopsis from photooxidative stress. Proc Natl Acad Sci USA. 2003;100(8):4921–4926.

44

Farquhar GD, von Caemmerer S, Berry JA. Models of photosynthesis. Plant Physiol. 2001;125(1):42–45.

45

Wang X, Wang F, Sang Y, Liu H. Full-spectrum solar light activated photocatalysts for light chemical energy conversion. Adv Energy Mater. 2017;7(23):1700473.

46

Kami C, Lorrain S, Hornitschek P, Fankhauser C. Light-regulated plant growth and development. Curr Top Dev Biol. 2010;91:29–66.

47

Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. J Exp Bot. 2022;73(10):3157–3172.

48

Demmig-Adams B, Adams Iii W. Photoprotection and other responses of plants to high light stress. Annu Rev Plant Biol. 1992;43(1):599–626.

49

Han Y, Lee J, Haiping G, Kim K-H, Wanxi P, Bhardwaj N, Oh J-M, Brown RJC. Plant-based remediation of air pollution: A review. J Environ Manag. 2022;301:Article 113860.

50

Molnár VÉ, Simon E, Tóthmérész B, Ninsawat S, Szabó S. Air pollution induced vegetation stress—The air pollution tolerance index as a quick tool for city health evaluation. Ecol Indic. 2020;113:Article 106234.

51

Shannigrahi AS, Fukushima T, Sharma RC. Anticipated air pollution tolerance of some plant species considered for green belt development in and around an industrial/urban area in India: An overview. Int J Environ Stud. 2004;61(2):125–137.

52

Agbaire P, Esiefarienrhe E. Air pollution tolerance indices (apti) of some plants around Otorogun Gas Plant in Delta State, Nigeria. J Appl Sci Environ Manag. 2009;13(1):1–14.

53

Banerjee S, Banerjee A, Palit D. Morphological and biochemical study of plant species—A quick tool for assessing the impact of air pollution. J Clean Prod. 2022;339:Article 130647.

54
Gostin I. Air pollution stress and plant response. In: Kulshrestha U, Saxena P, editors. Plant responses to air pollution. Singapore: Springer Singapore; 2016. p. 99–117.
55
Bhugra S, Mishra D, Anupama A, Chaudhury S, Lall B, Chugh A, Chinnusamy V. Deep convolutional neural networks based framework for estimation of stomata density and structure from microscopic images. Paper presented at: Proceedings of the European Conference on Computer Vision (ECCV) Workshops; 2018 Sep 8–14; Munich, Germany.
56
Word Health Organization. Air pollution. https://www.who.int/health-topics/air-pollution
57

Sanaeifar A, Zhang W, Chen H, Zhang D, Li X, He Y. Study on effects of airborne Pb pollution on quality indicators and accumulation in tea plants using Vis-NIR spectroscopy coupled with radial basis function neural network. Ecotoxicol Environ Saf. 2022;229:Article 113056.

58

Maxwell K, Johnson GN. Chlorophyll fluorescence—A practical guide. J Exp Bot. 2000;51(345):659–668.

59

Sun D, Xu Y, Cen H. Optical sensors: Deciphering plant phenomics in breeding factories. Trends Plant Sci. 2022;27(2):209–210.

60

Feng X, Zhan Y, Wang Q, Yang X, Yu C, Wang H, Tang Z, Jiang D, Peng C, He Y. Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. Plant J. 2020;101(6):1448–1461.

61

Feng X, Yu Z, Fang H, Jiang H, Yang G, Chen L, Zhou X, Hu B, Qin C, Hu G, et al. Plantorganelle hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy. Nat Plants. 2023;9(10):1760–1775.

62
Giménez C, Gallardo M, Thompson RB. Plant–water relations. In: Reference module in earth systems and environmental sciences. Amsterdam (Netherlands): Elsevier; 2013.
63

Ye Z-H. Vascular tissue differentiation and pattern formation in plants. Annu Rev Plant Biol. 2002;53(1):183–202.

64

Fukuda H, Ohashi-Ito K. Vascular tissue development in plants. Curr Top Dev Biol. 2019;131:141–160.

65

Tyree MT, Zimmermann MH. Xylem structure and the ascent of sap. Heidelberg (Germany): Springer Science & Business Media; 2013.

66

Ding Y, Yang S. Surviving and thriving: How plants perceive and respond to temperature stress. Dev Cell. 2022;57(8):947–958.

67

Sweetlove LJ, Ratcliffe RG. Flux-balance modeling of plant metabolism. Front Plant Sci. 2011;2:38.

68
Larkindale J, Mishkind M, Vierling E. Plant responses to high temperature. In: Jenks M, Hasegawa PM, editors. Plant abiotic stress. Oxford Ames Carlton: Blackwell Publishing; 2005. p. 100–134.
69

Mishra D, Shekhar S, Chakraborty S, Chakraborty N. High temperature stress responses and wheat: Impacts and alleviation strategies. Environ Exp Bot. 2021;190:Article 104589.

70

Zandalinas SI, Mittler R, Balfagón D, Arbona V, Gómez-Cadenas A. Plant adaptations to the combination of drought and high temperatures. Physiol Plant. 2018;162(1):2–12.

71

Fu JJ, Liu J, Yang LY, Miao YJ, Xu YF. Effects of low temperature on seed germination, early seedling growth and antioxidant systems of the wild Elymus nutans Griseb. J Agric Sci Technol. 2017;19(5):1113–1125.

72

Hussain HA, Hussain S, Khaliq A, Ashraf U, Anjum SA, Men S, Wang L. Chilling and drought stresses in crop plants: Implications, cross talk, and potential management opportunities. Front Plant Sci. 2018;9:393.

73

Yadav SK. Cold stress tolerance mechanisms in plants. A review. Agron Sustain Dev. 2010;30(3):515–527.

74

Thomashow MF. Role of cold-responsive genes in plant freezing tolerance. Plant Physiol. 1998;118(1):1–8.

75

Knight MR, Knight H. Low-temperature perception leading to gene expression and cold tolerance in higher plants. New Phytol. 2012;195(4):737–751.

76

Wang F. Research progress of phenotype and physiological response mechanism of plants under low temperature stress. Mol Plant Breed. 2018;17:5144–5153.

77

Kim HK, Park J, Hwang I. Investigating water transport through the xylem network in vascular plants. J Exp Bot. 2014;65(7):1895–1904.

78

Vandegehuchte MW, Steppe K. Sap-flux density measurement methods: Working principles and applicability. Funct Plant Biol. 2013;40(3):213–223.

79

Green S, Clothier B, Jardine B. Theory and practical application of heat pulse to measure sap flow. Agron J. 2003;95(6):1371–1379.

80

Ritman K, Milburn J. Acoustic emissions from plants: Ultrasonic and audible compared. J Exp Bot. 1988;39(9):1237–1248.

81

Dostál P, Sriwongras P, Trojan V. Detection of acoustic emission characteristics of plant according to water stress condition. Acta Univ Agric Silvic Mendel Brun. 2016;64(5):1465–1471.

82

De Roo L, Vergeynst LL, De Baerdemaeker NJ, Steppe K. Acoustic emissions to measure drought-induced cavitation in plants. Appl Sci. 2016;6(3):71.

83

Chai Y, Chen C, Luo X, Zhan S, Kim J, Luo J, Wang X, Hu Z, Ying Y, Liu X. Cohabiting plant-wearable sensor in situ monitors water transport in plant. Adv Sci. 2021;8(10):2003642.

84

Chen R, Ren S, Li S, Han D, Qin K, Jia X, Zhou H, Gao Z. Recent advances and prospects in wearable plant sensors. Rev Environ Sci Biotechnol. 2023;22(4):933–968.

85

Zwieniecki MA, Melcher PJ, Ahrens ET. Analysis of spatial and temporal dynamics of xylem refilling in Acer rubrum L. using magnetic resonance imaging. Front Plant Sci. 2013;4:265.

86

Hubeau M, Steppe K. Plant-PET scans: In vivo mapping of xylem and phloem functioning. Trends Plant Sci. 2015;20(10):676–685.

87

Grierson C, Nielsen E, Ketelaarc T, Schiefelbein J. Root hairs. Arabidopsis Book. 2014;12:e0172.

88

Gupta A, Rico-Medina A, Caño-Delgado AI. The physiology of plant responses to drought. Science. 2020;368(6488):266–269.

89

Loreti E, van Veen H, Perata P. Plant responses to flooding stress. Curr Opin Plant Biol. 2016;33:64–71.

90

Cohen I, Zandalinas SI, Huck C, Fritschi FB, Mittler R. Meta-analysis of drought and heat stress combination impact on crop yield and yield components. Physiol Plant. 2021;171(1):66–76.

91

Agurla S, Gahir S, Munemasa S, Murata Y, Raghavendra AS. Mechanism of stomatal closure in plants exposed to drought and cold stress. Adv Exp Med Biol. 2018;1081:215–232.

92

Farooq M, Wahid A, Kobayashi N, Fujita D, Basra SMA. Plant drought stress: Effects, mechanisms and management. Agron Sustain Dev. 2009;29(1):185–212.

93

Basu S, Ramegowda V, Kumar A, Pereira A. Plant adaptation to drought stress. F1000Res. 2016;5:F1000.

94

Kusvuran S. Microalgae (Chlorella vulgaris Beijerinck) alleviates drought stress of broccoli plants by improving nutrient uptake, secondary metabolites, and antioxidative defense system. Hortic Plant J. 2021;7(3):221–231.

95

Danzi D, De Paola D, Petrozza A, Summerer S, Cellini F, Pignone D, Janni M. The use of near-infrared imaging (NIR) as a fast non-destructive screening tool to identify drought-tolerant wheat genotypes. Agriculture. 2022;12(4):537.

96

Sasidharan R, Bailey-Serres J, Ashikari M, Atwell BJ, Colmer TD, Fagerstedt K, Fukao T, Geigenberger P, Hebelstrup KH, Hill RD, et al. Community recommendations on terminology and procedures used in flooding and low oxygen stress research. New Phytol. 2017;214(4):1403–1407.

97

Tian L-x, Zhang Y-c, Chen P-l, Zhang F-f, Li J, Yan F, Dong Y, Feng BL, Li J, Yan F, et al. How does the waterlogging regime affect crop yield? A global meta-analysis. Front Plant Sci. 2021;12:Article 634898.

98

Jia W, Ma M, Chen J, Wu S. Plant morphological, physiological and anatomical adaption to flooding stress and the underlying molecular mechanisms. Int J Mol Sci. 2021;22(3):1088.

99

Haj-Amor Z, Araya T, Kim D-G, Bouri S, Lee J, Ghiloufi W, Yang Y, Kang H, Jhariya MK, Banerjee A, et al. Soil salinity and its associated effects on soil microorganisms, greenhouse gas emissions, crop yield, biodiversity and desertification: A review. Sci Total Environ. 2022;843:Article 156946.

100
Bunt AC. Microelements. In: Bunt AC, editors. Media and mixes for container-grown plants: A manual on the preparation and use of growing media for pot plants. Dordrecht: Springer Netherlands; 1988. p. 151–173.
101
Pandey R, Krishnapriya V, Bindraban PS. Biochemical nutrient pathways in plants applied as foliar spray: Phosphorus and iron. Washington, VFRC, VFRC Report 2013/1; 2013.
102

Pandey R, Vengavasi K, Hawkesford MJ. Plant adaptation to nutrient stress. Plant Physiol Rep. 2021;26(4):583–586.

103

Bouain N, Krouk G, Lacombe B, Rouached H. Getting to the root of plant mineral nutrition: Combinatorial nutrient stresses reveal emergent properties. Trends Plant Sci. 2019;24(6):542–552.

104

Arif Y, Singh P, Siddiqui H, Bajguz A, Hayat S. Salinity induced physiological and biochemical changes in plants: An omic approach towards salt stress tolerance. Plant Physiol Biochem. 2020;156:64–77.

105

Maathuis FJM. Physiological functions of mineral macronutrients. Curr Opin Plant Biol. 2009;12(3):250–258.

106
Fageria NK, Nascente AS: Chapter six—Management of soil acidity of south American soils for sustainable crop production. In: Sparks DL, editor. Advances in agronomy. Amsterdam (Netherlands): Academic Press; 2014. vol. 128, p. 221–275.
107

Kochhar S, Gujral SK. Plant physiology: Theory and applications. Cambridge (UK): Cambridge University Press; 2020.

108

Espejo-Garcia B, Malounas I, Mylonas N, Kasimati A, Fountas S. Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies. Comput Electron Agric. 2022;196:Article 106868.

109

Amtmann A, Armengaud P. Effects of N, P, K and S on metabolism: New knowledge gained from multi-level analysis. Curr Opin Plant Biol. 2009;12(3):275–283.

110

We Z, Pan X, Zhao Q, Zhao T. Plant growth, antioxidative enzyme, and cadmium tolerance responses to cadmium stress in Canna orchioides. Hortic Plant J. 2021;7(3):256–266.

111

Li X, Zhou D. A meta-analysis on phenotypic variation in cadmium accumulation of Rice and wheat: Implications for food cadmium risk control. Pedosphere. 2019;29(5):545–553.

112

Ghori N-H, Ghori T, Hayat M, Imadi S, Gul A, Altay V, Ozturk M. Heavy metal stress and responses in plants. Int J Environ Sci Technol. 2019;16(3):1807–1828.

113

Xie LH, Tang SQ, Wei XJ, Shao GN, Jiao GA, Sheng ZH, Luo J, Hu PS. The cadmium and lead content of the grain produced by leading Chinese rice cultivars. Food Chem. 2017;217:217–224.

114

Singh DJ, Kalamdhad A. Effects of heavy metals on soil, plants, human health and aquatic life. Int J Res Chem Environ. 2011;1(2):15–21.

115

Kuijken RC, van Eeuwijk FA, Marcelis LF, Bouwmeester HJ. Root phenotyping: From component trait in the lab to breeding. J Exp Bot. 2015;66(18):5389–5401.

116

Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays). Plant Physiol. 2021;187(2):739–757.

117

Herrero-Huerta M, Raumonen P, Gonzalez-Aguilera D. 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography. Front Plant Sci. 2022;13:Article 986856.

118

Jahnke S, Menzel MI, Van Dusschoten D, Roeb GW, Bühler J, Minwuyelet S, Blümler P, Temperton VM, Hombach T, Streun M, et al. Combined MRI–PET dissects dynamic changes in plant structures and functions. Plant J. 2009;59(4):634–644.

119

Mooney SJ, Pridmore TP, Helliwell J, Bennett MJ. Developing X-ray computed tomography to non-invasively image 3-D root systems architecture in soil. Plant Soil. 2012;352(1):1–22.

120
Higley LG, Browde JA, Higley PM. Moving towards new understandings of biotic stress and stress interactions. In: Buxton DR, Shibles R, Forsberg RA, Blad BL, Asay KH, Paulsen GM, Wilson RF, editors. International Crop Science I. Madison: CSSA; 1993. p. 749–754.
121

Mittler R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 2006;11(1):15–19.

122

Mittler R, Blumwald E. Genetic engineering for modern agriculture: Challenges and perspectives. Annu Rev Plant Biol. 2010;61(1):443–462.

123

Enders TA, St. Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM, Hirsch CD. Classifying cold-stress responses of inbred maize seedlings using RGB imaging. Plant Direct. 2019;3(1):Article e00104.

124

Tackenberg O. A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis. Ann Bot. 2007;99(4):777–783.

125

Rahaman MM, Chen D, Gillani Z, Klukas C, Chen M. Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci. 2015;6:619.

126

Neto AJS, Lopes DC, Pinto FA, Zolnier S. Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves. Biosyst Eng. 2017;155:124–133.

127
Qin JW, Monje O, Nugent MR, Finn JR, O'Rourke AE, Fritsche RF, Baek I, Chan DE, Kim MS. Development of a hyperspectral imaging system for plant health monitoring in space crop production. Paper presented at: Conference on Sensing for Agriculture and Food Quality and Safety XIV; 2022 Apr 3–Jun 12; Florida, USA.
128

Cui LH, Yan LJ, Zhao XH, Yuan L, Jin J, Zhang JC. Detection and discrimination of tea plant stresses based on hyperspectral imaging technique at a canopy level. Phyton Int J Exp Bot. 2021;90(2):621–634.

129

Xu R, Li CY, Paterson AH. Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PLOS ONE. 2019;14(2):e0205083.

130

Mishra P, Asaari MSM, Herrero-Langreo A, Lohumi S, Diezma B, Scheunders P. Close range hyperspectral imaging of plants: A review. Biosyst Eng. 2017;164:49–67.

131

Pineda M, Barón M, Pérez-Bueno M-L. Thermal imaging for plant stress detection and phenotyping. Remote Sens. 2020;13(1):68.

132

Khanal S, Fulton J, Shearer S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput Electron Agric. 2017;139:22–32.

133

Yu S, Zhang N, Kaiser E, Li G, An D, Sun Q, Chen W, Liu W, Luo W. Integrating chlorophyll fluorescence parameters into a crop model improves growth prediction under severe drought. Agric For Meteorol. 2021;303:Article 108367.

134

Cendrero-Mateo MP, Moran MS, Papuga SA, Thorp KR, Alonso L, Moreno J, Ponce-Campos G, Rascher U, Wang G. Plant chlorophyll fluorescence: Active and passive measurements at canopy and leaf scales with different nitrogen treatments. J Exp Bot. 2015;67(1):275–286.

135

Yang J, Song S, Du L, Shi S, Gong W, Sun J, Chen B. Analyzing the effect of fluorescence characteristics on leaf nitrogen concentration estimation. Remote Sens. 2018;10(9):1402.

136

Möller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, Ostrovsky V, Sprintsin M, Cohen S. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot. 2007;58(4):827–838.

137

Quan L, Tan P, Zeng G, Yuan L, Wang J, Kang SB. Image-based plant modeling. ACM Trans Graph. 2006;25(3):599–604.

138

Kim JJ, Allison LK, Andrew TL. Vapor-printed polymer electrodes for long-term, on-demand health monitoring. Sci Adv. 2019;5(3):eaaw0463.

139

Su YJ, Wu FF, Ao ZR, Jin SC, Qin F, Liu BX, Pang SX, Liu LL, Guo QH. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods. 2019;15:11.

140

Perez-Sanz F, Navarro PJ, Egea-Cortines M. Plant phenomics: An overview of image acquisition technologies and image data analysis algorithms. Gigascience. 2017;6(11):1–18.

141

Li YL, Wen WL, Miao T, Wu S, Yu ZT, Wang XD, Guo XY, Zhao CJ. Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning. Comput Electron Agric. 2022;193:106702.

142

Gomez FE, Carvalho G Jr, Shi F, Muliana AH, Rooney WL. High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum. Plant Methods. 2018;14:59.

143

Okochi T, Hoshino Y, Fujii H, Mitsutani T. Nondestructive tree-ring measurements for Japanese oak and Japanese beech using micro-focus X-ray computed tomography. Dendrochronologia. 2007;24(2):155–164.

144

Blümich B, Callaghan PT. Principles of nuclear magnetic resonance microscopy. New Jersey (USA): Wiley Online Library; 1995.

145

Köckenberger W, De Panfilis C, Santoro D, Dahiya P, Rawsthorne S. High resolution NMR microscopy of plants and fungi. J Microsc. 2004;214(2):182–189.

146

Zhou YF, Maitre R, Hupel M, Trotoux G, Penguilly D, Mariette F, Bousset L, Chevre AM, Parisey N. An automatic non-invasive classification for plant phenotyping by MRI images: An application for quality control on cauliflower at primary meristem stage. Comput Electron Agric. 2021;187:106303.

147

Windt CW, Vergeldt FJ, De Jager PA, Van As H. MRI of long-distance water transport: A comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. Plant Cell Environ. 2006;29(9):1715–1729.

148

Galieni A, D’Ascenzo N, Stagnari F, Pagnani G, Xie QG, Pisante M. Past and future of plant stress detection: An overview from remote sensing to positron emission tomography. Front Plant Sci. 2021;11:609155.

149

Arino-Estrada G, Mitchell GS, Saha P, Arzani A, Cherry SR, Blumwald E, Kyme AZ. Imaging salt uptake dynamics in plants using PET. Sci Rep. 2019;9(1):18626.

150

Kuchenbuch RO, Ingram KT. Image analysis for non-destructive and non-invasive quantification of root growth and soil water content in rhizotrons. J Plant Nutr Soil Sci. 2002;165(5):573–581.

151

Amato M, Basso B, Celano G, Bitella G, Morelli G, Rossi R. In situ detection of tree root distribution and biomass by multi-electrode resistivity imaging. Tree Physiol. 2008;28(10):1441–1448.

152

Whalley WR, Binley A, Watts C, Shanahan P, Dodd IC, Ober E, Ashton R, Webster C, White R, Hawkesford MJ. Methods to estimate changes in soil water for phenotyping root activity in the field. Plant Soil. 2017;415(1):407–422.

153
Wang Q, Komarov S, Mathews AJ, Li K, Topp C, O'Sullivan JA, Tai Y-C. Combined 3D PET and optical projection tomography techniques for plant root phenotyping. arXiv. 2015. https://doi.org/10.48550/arXiv.1501.00242
Plant Phenomics
Article number: 0180
Cite this article:
Wu L, Shao H, Li J, et al. Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View. Plant Phenomics, 2024, 6: 0180. https://doi.org/10.34133/plantphenomics.0180

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Received: 29 October 2023
Accepted: 29 March 2024
Published: 22 May 2024
© 2024 Libin Wu et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

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