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Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.)

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Abstract

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0–3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.

Graphical abstract

Key message

Chemical priming of H2O2 with LED lights regulates the cannabis plant growth. Use of Pareto chart and normal plots to rank the input factor, their impact, and efficiency in percentage. Use of optimizing tools like contour plots, surface plots, and response optimizers to optimize H2O2 and LED lights for cannabis. Data validation and prediction using AI/ML-based MLP, RF, and XGBoost models.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available and can be provided on reasonable request.

References

  • Aasim M, Ali SA, Bekiş P, Nadeem MA (2022a) Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms. Vitr Cell Dev Biol. https://doi.org/10.1007/s11627-022-10312-6

    Article  Google Scholar 

  • Aasim M, Katirci R, Baloch F, Mustafa Z, Bakhsh A, Nadeem M, Ali S, Hatipoğlu R, Çiftçi V, Habyarimana E (2022b) Innovation in the breeding of common bean through a combined approach of in vitro regeneration and machine learning algorithms. Front Genet. https://doi.org/10.3389/fgene.2022.897696

    Article  PubMed  PubMed Central  Google Scholar 

  • Aasim M, Katırcı R, Akgur O, Yildirim B, Mustafa Z, Nadeem MA, Baloch FS, Karakoy T, Yılmaz G (2022c) Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.). Ind Crops Prod 181:114801

    Article  Google Scholar 

  • Aasim M, Akin F, Ali SA, Taskin MB, Colak MS, Khawar KM (2023a) Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L.). Physiol Mol Biol Plants. https://doi.org/10.1007/s12298-023-01282-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Aasim M, Ali SA, Altaf MT, Ali A, Nadeem MA, Baloch FS (2023b) Artificial neural network and decision tree facilitated prediction and validation of cytokinin-auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.). Plant Cell Tissue Organ Cult. https://doi.org/10.1007/s11240-023-02498-3

    Article  Google Scholar 

  • Aasim M, Ali SA, Aydin S, Bakhsh A, Sogukpinar C, Karatas M, Khawar KM, Aydin ME (2023c) Artificial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-25081-3

    Article  Google Scholar 

  • Aasim M, Ayhan A, Katırcı R, Acar AŞ, Ali SA (2023d) Computing artificial neural network and genetic algorithm for the feature optimization of basal salts and cytokinin-auxin for in vitro organogenesis of royal purple (Cotinus coggygria Scop). Ind Crops Prod 199:116718

    Article  CAS  Google Scholar 

  • Agarwal S, Pandey V (2004) Antioxidant enzyme responses to NaCl stress in Cassia angustifolia. Biol Plant 48:555–560

    Article  CAS  Google Scholar 

  • Aggarwal CC (2018) Neural networks and deep learning. Springer, Chem

    Book  Google Scholar 

  • Angelini R, Federico R (1989) Histochemical evidence of polyamine oxidation and generation of hydrogen peroxide in the cell wall. J Plant Physiol 135:212–217

    Article  CAS  Google Scholar 

  • Angelini R, Manes F, Federico R (1990) Spatial and functional correlation between diamine-oxidase and peroxidase activities and their dependence upon de-etiolation and wounding in chick-pea stems. Planta 182:89–96

    Article  CAS  PubMed  Google Scholar 

  • Bashir K, Todaka D, Rasheed S, Matsui A, Ahmad Z, Sako K, Utsumi Y, Vu AT, Tanaka M, Takahashi S (2022) Ethanol-mediated novel survival strategy against drought stress in plants. Plant Cell Physiol 63:1181–1192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bello-Bello JJ, Martínez-Estrada E, Caamal-Velázquez JH, Morales-Ramos V (2016) Effect of LED light quality on in vitro shoot proliferation and growth of vanilla (Vanilla planifolia Andrews). African J Biotechnol 15:272–277

    Article  CAS  Google Scholar 

  • Bewley JD, Bradford K, Hilhorst H (2012) Seeds: physiology of development, germination and dormancy. Springer, New York

    Google Scholar 

  • Bhardwaj RD, Singh N, Sharma A, Joshi R, Srivastava P (2021) Hydrogen peroxide regulates antioxidant responses and redox related proteins in drought stressed wheat seedlings. Physiol Mol Biol Plants 27:151–163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bilbao A, Spanagel R (2022) Medical cannabinoids: a pharmacology-based systematic review and meta-analysis for all relevant medical indications. BMC Med 20:259

    Article  PubMed  PubMed Central  Google Scholar 

  • Burescu L, Cachita D, Craciun C (2015) The effect of different wavelengths LED lighting on the growth of spruce (Picea abies L.) plantlets. Rom Biotechnol Lett 20:10911–10920

    Google Scholar 

  • Chaari M, Elhadef K, Akermi S, Ben HH, Fourati M, ChakchoukMtibaa A, Sarkar T, Shariati MA, Rebezov M, D’Amore T (2022) Multiobjective response and chemometric approaches to enhance the phytochemicals and biological activities of beetroot leaves: an unexploited organic waste. Biomass Convers Biorefin 13:15067–15081

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794

  • Cho KH, Laux VY, Wallace-Springer N, Clark DG, Folta KM, Colquhoun TA (2019) Effects of light quality on vegetative cutting and in vitro propagation of coleus (Plectranthus scutellarioides). HortScience 54:926–935

    Article  Google Scholar 

  • da Silva RR, de Souza RR, Coimbra M, Nery F, Alvarenga A, Paiva R (2020) Light quality on growth and phenolic compounds accumulation in Moringa oleifera L. grown in vitro. Comun Sci 11:e3313–e3313

    Article  Google Scholar 

  • de Souza RR, de Paiva PD, O, Silva RR da, Reis MV dos, Nery FC, Paiva R (2016) Optimization of jenipapo in vitro seed germination process. Ciência e Agrotecnologia 40:658–664

    Article  Google Scholar 

  • Gao Z, Luo Z, Zhang W, Lv Z, Xu Y (2020) Deep learning application in plant stress imaging: a review. AgriEngineering 2:29

    Article  Google Scholar 

  • Genze N, Bharti R, Grieb M, Schultheiss SJ, Grimm DG (2020) Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops. Plant Methods 16:1–11

  • Gong Y, Toivonen PMA, Lau OL, Wiersma PA (2001) Antioxidant system level in ’Braeburn’ apple is related to its browning disorder. Bot Bull Acad Sin 42:259–264

    CAS  Google Scholar 

  • Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424

    Article  Google Scholar 

  • Hasan MM, Bashir T, Ghosh R, Lee SK, Bae H (2017) An overview of LEDs’ effects on the production of bioactive compounds and crop quality. Molecules 22:1420

    Article  PubMed  PubMed Central  Google Scholar 

  • Havir EA, McHale NA (1987) Biochemical and developmental characterization of multiple forms of catalase in tobacco leaves. Plant Physiol 84:450–455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hesami M, Naderi R, Tohidfar M (2019) Modeling and optimizing in vitro sterilization of chrysanthemum via multilayer perceptron-non-dominated sorting genetic algorithm-II (MLP-NSGAII). Front Plant Sci 10:1–13

    Google Scholar 

  • Hesami M, Pepe M, Alizadeh M, Rakei A, Baiton A, Phineas Jones AM (2020) Recent advances in Cannabis biotechnology. Ind Crops Prod 158:113026

    Article  CAS  Google Scholar 

  • Hesami M, Najafabadi MY, Adamek K, Torkamaneh D, Maxwell A, Jones P (2021a) Synergizing off-target predictions for In silico şnsights of CENH3 knockout in cannabis through CRISPR / Cas. Molecules 26(7):2053. https://doi.org/10.3390/molecules26072053

  • Hesami M, Pepe M, Monthony AS, Baiton A, Phineas Jones AM (2021b) Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.). Ind Crops Prod 170:113753

    Article  CAS  Google Scholar 

  • Hesami M, Alizadeh M, Jones AMP, Torkamaneh D (2022) Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol. https://doi.org/10.1007/s00253-022-11963-6

    Article  PubMed  Google Scholar 

  • Jafari M, Shahsavar A (2020) The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress. PLoS ONE 15:e0240427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jaskulak M, Grobelak A, Vandenbulcke F (2020) Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via response surface methodology and artificial neural networks during assisted phytoremediation with sewage sludge. Int J Phytoremediation 22:1321–1330

    Article  CAS  PubMed  Google Scholar 

  • Kamil A, Khan MA, Aasim M, Khan NZ, Khan RS, Jamal M, Ahmad W, Khan MA, Jalil F (2019) Detection of ROS and translocation of ERP-57 in apoptotic induced human neuroblastoma (SH-SY5Y) cells. Biocell 43:167–174

    Article  CAS  Google Scholar 

  • Kapoor S, Raghuvanshi R, Bhardwaj P, Sood H, Saxena S, Chaurasia OP (2018) Influence of light quality on growth, secondary metabolites production and antioxidant activity in callus culture of Rhodiola imbricata Edgew. J Photochem Photobiol B 183:258–265

    Article  CAS  PubMed  Google Scholar 

  • Karabal E, Yücel M, Öktem HA (2003) Antioxidant responses of tolerant and sensitive barley cultivars to boron toxicity. Plant Sci 164:925–933

    Article  CAS  Google Scholar 

  • Kasman M, Riyanti A, Salmariza S, Aslamia RTSS (2019) Response surface methodology approach for analysis of phytoremediation process of Pb (II) from aqueous solution using Echinodorus palaefolius. IOP Conf Ser 546:22009

    Article  CAS  Google Scholar 

  • Katirci R (2015) Statistical approach to optimizing a Zn–Ni bath containing ed and tea. Surf Rev Lett 22:1550015

    Article  Google Scholar 

  • Katırcı R, Yılmaz EK, Kaynar O, Zontul M (2021) Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods. Surf Coatings Technol 422:127571

    Article  Google Scholar 

  • Keijok WJ, Pereira RHA, Alvarez LAC, Prado AR, da Silva AR, Ribeiro J, de Oliveira JP, Guimarães MCC (2019) Controlled biosynthesis of gold nanoparticles with Coffea arabica using factorial design. Sci Rep 9:1–10

    Article  CAS  Google Scholar 

  • Khan T, Ullah MA, Garros L, Hano C, Abbasi BH (2019) Synergistic effects of melatonin and distinct spectral lights for enhanced production of anti-cancerous compounds in callus cultures of Fagonia indica. J Photochem Photobiol B 190:163–171

    Article  CAS  PubMed  Google Scholar 

  • Kim K, Kook H, Jang Y, Lee W, Kamala-Kannan S, Chae J, Lee K (2013) The effect of blue-light-emitting diodes on antioxidant properties and resistance to Botrytis cinerea in tomato. J Plant Pathol Microbiol 4:203

    CAS  Google Scholar 

  • Klimek-Szczykutowicz M, Prokopiuk B, Dziurka K, Pawłowska B, Ekiert H, Szopa A (2022) The influence of different wavelengths of LED light on the production of glucosinolates and phenolic compounds and the antioxidant potential in in vitro cultures of Nasturtium officinale (watercress). Plant Cell Tissue Organ Cult 149:113–122

    Article  CAS  Google Scholar 

  • León-López L, Escobar-Zúñiga Y, Salazar-Salas NY, Mora Rochín S, Cuevas-Rodríguez EO, Reyes-Moreno C, Milán-Carrillo J (2020) Improving polyphenolic compounds: antioxidant activity in chickpea sprouts through elicitation with hydrogen peroxide. Foods 9:1791

    Article  PubMed  PubMed Central  Google Scholar 

  • Lim CH, Guan TS, Chan Hong E, Lit Chow Y, Lynn CB, Subramaniam S (2020) Effect of different LED lights spectrum on the in vitro germination of gac seed (Momordica cochinchinensis). Aust J Crop Sci 14:1715–1722

    Article  CAS  Google Scholar 

  • Lone AB, Unemoto LK, Ferrari EAP, Takahashi LSA, de Faria RT (2014) The effects of light wavelength and intensity on the germination of pitaya seed genotypes. Aust J Crop Sci 8:1475–1480

    Google Scholar 

  • Ma Y, Xu A, Cheng Z-MM (2021) Effects of light emitting diode lights on plant growth, development and traits a meta-analysis. Hortic Plant J 7:552–564

    Article  Google Scholar 

  • Martins N, Barros L, Santos-Buelga C, Henriques M, Silva S, Ferreira ICFR (2015) Evaluation of bioactive properties and phenolic compounds in different extracts prepared from Salvia officinalis L. Food Chem 170:378–385

    Article  CAS  PubMed  Google Scholar 

  • Matsui A, Todaka D, Tanaka M, Mizunashi K, Takahashi S, Sunaoshi Y, Tsuboi Y, Ishida J, Bashir K, Kikuchi J (2022) Ethanol induces heat tolerance in plants by stimulating unfolded protein response. Plant Mol Biol 110:131–145

    Article  CAS  PubMed  Google Scholar 

  • Mirza K, Aasim M, Katırcı R, Karataş M, Ali SA (2022) Machine learning and artificial neural networks-based approach to model and optimize ethyl methanesulfonate and sodium azide induced in vitro regeneration and morphogenic traits of water hyssops (Bacopa monnieri L.). J Plant Growth Regul 42:3471–3485

    Article  Google Scholar 

  • Mishra B, Kumar N, Mukhtar MS (2019) Systems biology and machine learning in plant–pathogen interactions. Mol Plant-Microbe Interact 32:45–55

    Article  CAS  PubMed  Google Scholar 

  • Mohamad Thani NS, Mohd Ghazi R, Abdul Wahab IR, Mohd Amin MF, Hamzah Z, Nik Yusoff NR (2020) Optimization of phytoremediation of nickel by Alocasia puber using response surface methodology. Water 12:2707

    Article  Google Scholar 

  • Mahood EH, Kruse LH, Moghe GD (2020) Machine learning: a powerful tool for gene function prediction in plants. Appl Plant Sci 8:e11376

  • Monostori I, Heilmann M, Kocsy G, Rakszegi M, Ahres M, Altenbach SB, Szalai G, Pál M, Toldi D, Simon-Sarkadi L (2018) LED lighting–modification of growth, metabolism, yield and flour composition in wheat by spectral quality and intensity. Front Plant Sci 9:605

    Article  PubMed  PubMed Central  Google Scholar 

  • Monthony AS, Page SR, Hesami M, Jones AMP (2021) The past, present and future of Cannabis sativa tissue culture. Plants 10:185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morrow RC (2008) LED lighting in horticulture. HortScience 43:1947–1950

    Article  Google Scholar 

  • Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497

    Article  CAS  Google Scholar 

  • Myo EM, Ge B, Ma J, Cui H, Liu B, Shi L, Jiang M, Zhang K (2019) Indole-3-acetic acid production by Streptomyces fradiae NKZ-259 and its formulation to enhance plant growth. BMC Microbiol 19:1–14

    Article  CAS  Google Scholar 

  • Nazir M, Ullah MA, Younas M, Siddiquah A, Shah M, Giglioli-Guivarc’h N, Hano C, Abbasi BH, (2020) Light-mediated biosynthesis of phenylpropanoid metabolites and antioxidant potential in callus cultures of purple basil (Ocimum basilicum L. var purpurascens). Plant Cell, Tissue Organ Cult 142:107–120

    Article  CAS  Google Scholar 

  • Nik Muhammad Nasir NN, Khandaker MM, Mohd KS, Badaluddin NA, Osman N, Mat N (2021) Effect of hydrogen peroxide on plant growth, photosynthesis, leaf histology and rubisco gene expression of the Ficus deltoidea Jack Var. Deltoidea Jack J Plant Growth Regul 40:1950–1971

    Article  CAS  Google Scholar 

  • Olvera-González E, Alaniz-Lumbreras D, Ivanov-Tsonchev R, Villa-Hernández J, de la Rosa-Vargas I, López-Cruz I, Silos-Espino H, Lara-Herrera A (2013) Chlorophyll fluorescence emission of tomato plants as a response to pulsed light based LEDs. Plant Growth Regul 69:117–123

    Article  Google Scholar 

  • Paparella S, Araújo SS, Rossi G, Wijayasinghe M, Carbonera D, Balestrazzi A (2015) Seed priming: state of the art and new perspectives. Plant Cell Rep 34:1281–1293

    Article  CAS  PubMed  Google Scholar 

  • Pavlov YL (2019) Random forests. Random. VSP, 2000

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine Learning in {P}ython. J Mach Learn Res 12:2825–2830

  • Pepe M, Hesami M, Jones AMP (2021a) Machine learning-mediated development and optimization of disinfection protocol and scarification method for improved in vitro germination of Cannabis seeds. Plants 10:2397

    Article  PubMed  PubMed Central  Google Scholar 

  • Pepe M, Hesami M, Small F, Jones AMP (2021b) Comparative analysis of machine learning and evolutionary optimization algorithms for precision micropropagation of Cannabis sativa: prediction and validation of in vitro shoot growth and development based on the optimization of light and carbohydrate sou. Front Plant Sci. https://doi.org/10.3389/fpls.2021.757869

    Article  PubMed  PubMed Central  Google Scholar 

  • Phat P, Ju H-J, Noh J, Lim J, Seong M, Chon H, Jeong J, Kwon S, Kim T (2017) Effects of hydropriming and explant origin on in vitro culture and frequency of tetraploids in small watermelons. Hortic Environ Biotechnol 58:495–502

    Article  Google Scholar 

  • Prieto P, Pineda M, Aguilar M (1999) Spectrophotometric quantitation of antioxidant capacity through the formation of a phosphomolybdenum complex: specific application to the determination of vitamin E. Anal Biochem 269:337–341

    Article  CAS  PubMed  Google Scholar 

  • Rico-Chávez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, Guevara-González RG, Hernandez-Escobedo Q (2022) Machine learning for plant stress modeling: a perspective towards hormesis management. Plants 11:970

    Article  PubMed  PubMed Central  Google Scholar 

  • Rock EM, Parker LA (2021) Constituents of Cannabis sativa. Cannabinoids and neuropsychiatric disorders. Springer, Chem, pp 1–13

    Google Scholar 

  • Salah SM, Yajing G, Dongdong C, Jie L, Aamir N, Qijuan H, Weimin H, Mingyu N, Jin H (2015) Seed priming with polyethylene glycol regulating the physiological and molecular mechanism in rice (Oryza sativa L.) under nano-ZnO stress. Sci Rep 5:14278

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Salehi M, Farhadi S, Moieni A, Safaie N, Hesami M (2021) A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture. Plant Methods 17:1–13

    Article  Google Scholar 

  • Salentijn EMJ, Zhang Q, Amaducci S, Yang M, Trindade LM (2015) New developments in fiber hemp (Cannabis sativa L.) breeding. Ind Crops Prod 68:32–41

    Article  Google Scholar 

  • Samiei S, Rasti P, Ly Vu J, Buitink J, Rousseau D (2020) Deep learning-based detection of seedling development. Plant Methods 16:1–11

  • Shah T, Latif S, Saeed F, Ali I, Ullah S, Alsahli AA, Jan S, Ahmad P (2021) Seed priming with titanium dioxide nanoparticles enhances seed vigor, leaf water status, and antioxidant enzyme activities in maize (Zea mays L.) under salinity stress. J King Saud Univ 33:101207

    Article  Google Scholar 

  • Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21:110–124

    Article  CAS  PubMed  Google Scholar 

  • Singleton VL, Rossi JA (1965) Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am J Enol Vitic 16:144–158

    Article  CAS  Google Scholar 

  • Škrubej U, Rozman Č, Stajnko D (2015) Assessment of germination rate of the tomato seeds using image processing and machine learning. Eur J Hortic Sci 80:68–75

    Article  Google Scholar 

  • Soltis PS, Nelson G, Zare A, Meineke EK (2020) Plants meet machines: prospects in machine learning for plant biology. Appl Plant Sci. https://doi.org/10.1002/aps3.11371

    Article  PubMed  PubMed Central  Google Scholar 

  • Sorokin A, Yadav NS, Gaudet D, Kovalchuk I (2021) Development and standardization of rapid and efficient seed germination protocol for Cannabis sativa. Bio-Protoc 11:e3875–e3875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tang D-S, Hamayun M, Khan AL, Shinwari ZK, Kim Y-H, Kang S-M, Lee J-H, Na C-I, Nawaz Y, Kang K-K (2010) Germination of some important weeds influenced by red light and nitrogenous compounds. Pak J Bot 42:3739–3745

    Google Scholar 

  • van Dijk ADJ, Kootstra G, Kruijer W, de Ridder D (2021) Machine learning in plant science and plant breeding. Iscience 24:101890

  • Van Rossum G, Drake FL (2009) Python 3 Reference Manual. CreateSpace, Scotts Valley, CA

  • Webb GI, Sammut C, Perlich C, Horváth T, Wrobel S, Korb KB, Noble WS, Leslie C, Lagoudakis MG, Quadrianto N, Buntine WL, Quadrianto N, Buntine WL, Getoor L, Namata G, Getoor L, Han XJ, J, Ting J-A, Vijayakumar S, Schaal S, Raedt L De, (2011) Leave-one-out cross-validation. Encyclopedia of machine learning. Springer, Boston, pp 600–601

    Google Scholar 

  • Xu Y, Yang M, Cheng F, Liu S, Liang Y (2020) Effects of LED photoperiods and light qualities on in vitro growth and chlorophyll fluorescence of Cunninghamia lanceolata. BMC Plant Biol 20:1–12

    Article  Google Scholar 

  • Ye Y, Tam NFY, Wong YS, Lu CY (2003) Growth and physiological responses of two mangrove species (Bruguiera gymnorrhiza and Kandelia candel) to waterlogging. Environ Exp Bot 49:209–221

    Article  Google Scholar 

  • Yildirim B, Aasim M, AYTAÇ S, (2023) Optimizing in vitro germination of primed industrial hemp (Cannabis sativa L.) seeds. Anatol J Bot 7:112–116

    Article  Google Scholar 

  • Yordanova RY, Christov KN, Popova LP (2004) Antioxidative enzymes in barley plants subjected to soil flooding. Environ Exp Bot 51:93–101

    Article  CAS  Google Scholar 

  • Younis M, Ahmed IAM, Ahmed KA, Yehia HM, Abdelkarim DO, Fickak A, El-Abedein AIZ, Alhamdan A, Elfeky A (2023) Pulsed electric field as a novel technology for fresh barhi date shelf-life extension: process optimization using response surface methodology. Horticulturae 9:155

    Article  Google Scholar 

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Funding

The present study was derived from Master thesis of Miss Buşra Yıldırım and the study was financially supported by The Scientific Research Council (BAP) of Sivas University of Science and Technology, Sivas, Türkiye (Grant Number:2022-YLTB-TBT-0001).

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Authors and Affiliations

  1. Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey

    Muhammad Aasim, Buşra Yıldırım & Muhammad Azhar Nadeem

  2. Department of Agricultural Biotechnology, Faculty of Agriculture, Erciyes University, Kayseri, Turkey

    Ahmet Say

  3. Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey

    Seyid Amjad Ali

  4. Institute of Hemp Researches, Ondokuz Mayis University, Samsun, Turkey

    Selim Aytaç

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  1. Muhammad Aasim
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Contributions

MA: Conceived idea, Supervision, Research designing, Data analysis, graphical work, Manuscript writing. BY: Conducted research work, data tabulation. AS: Biochemical and enzyme analysis. SAA: Tabulation of formulas, graphic figures, Review, Machine learning analysis. SA: Supervision, Article control, and editing. MAN: Article writing, Enzyme analysis.

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Aasim, M., Yıldırım, B., Say, A. et al. Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). Plant Mol Biol 114, 33 (2024). https://doi.org/10.1007/s11103-024-01427-y

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