Journals / Düzce Üniversitesi Süs ve Tıbbi Bitkiler Botanik Bahçesi Dergisi / 2025 / Cilt: 4 Sayı: 2

A Systematic Review on the Applications, Challenges, and Limitations of Smart and Precision Agriculture Technologies in Ornamental Plants

Süs Bitkilerinde Akıllı ve Hassas Tarım Teknolojilerinin Kullanım Alanları, Karşılaşılan Zorluklar ve Sınırlılıklar Üzerine Sistematik Bir Derleme

Nida Bayhan(BOLU ABANT İZZET BAYSAL ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ, BAHÇE BİTKİLERİ (DR))
Pages
32–53
Publication Date
2025-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that sensor-based irrigation systems, remote sensing, drone applications, IoT-based monitoring tools, artificial intelligence-supported decision-making mechanisms, and automation are the most prominent technologies in ornamental plant production.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study is a systematic review prepared to identify the application areas of smart and precision agriculture technologies in ornamental plants, the challenges encountered, and recommendations for future research.
📊
Key Findings & Data:The literature review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and studies obtained from selected databases were analyzed.
💡
Conclusion & Impact:The findings indicate that sensor-based irrigation systems, remote sensing, drone applications, IoT-based monitoring tools, artificial intelligence-supported decision-making mechanisms, and automation are the most prominent technologies in ornamental plant production.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:süs bitkileri · akıllı tarım · hassas tarım

Abstract

This study is a systematic review prepared to identify the application areas of smart and precision agriculture technologies in ornamental plants, the challenges encountered, and recommendations for future research. The literature review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and studies obtained from selected databases were analyzed. The findings indicate that sensor-based irrigation systems, remote sensing, drone applications, IoT-based monitoring tools, artificial intelligence-supported decision-making mechanisms, and automation are the most prominent technologies in ornamental plant production. These applications contribute to savings in water and nutrient management, improved energy efficiency, enhanced product quality, and reduced labor costs. However, high investment costs, the adaptation difficulties of small-scale producers, data security issues, lack of standardization, and insufficient technical knowledge emerge as major barriers limiting widespread adoption. For future research, the development of low-cost sensors, the expansion of artificial intelligence-based prediction models, and the strengthening of producer training programs are recommended. In conclusion, smart and precision farming technologies hold strategic potential for sustainable production in the ornamental plant sector; nevertheless, overcoming economic, technical, and institutional barriers is essential for realizing this potential.

Özet

Bu çalışma, süs bitkilerinde akıllı ve hassas tarım teknolojilerinin kullanım alanlarını, karşılaşılan zorlukları ve sınırlılıkları önerilerle birlikte ortaya koymak amacıyla hazırlanmış sistematik bir derlemedir. Çalışmada literatür taraması PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) yönergelerine uygun olarak gerçekleştirilmiş; seçilmiş veri tabanlarından elde edilen araştırmalar değerlendirilmiştir. Analizler, sensör tabanlı sulama sistemleri, uzaktan algılama, drone uygulamaları, IoT tabanlı izleme araçları, yapay zekâ destekli karar mekanizmaları ve otomasyonun süs bitkisi üretiminde öne çıkan teknolojiler olduğunu göstermektedir. Bu uygulamalar; su ve besin yönetiminde tasarruf, enerji verimliliği, ürün kalitesinde artış ve işgücü maliyetlerinde azalma sağlamaktadır. Ancak yüksek yatırım maliyetleri, küçük ölçekli üreticilerin uyum güçlüğü, veri güvenliği, standartlaşma eksiklikleri ve teknik bilgi yetersizlikleri yaygınlaşmayı sınırlayan temel engeller olarak öne çıkmaktadır. Gelecek araştırmalar için düşük maliyetli sensörlerin geliştirilmesi, yapay zekâ destekli tahmin modellerinin yaygınlaştırılması ve üretici eğitim programlarının güçlendirilmesi önerilmektedir. Sonuç olarak, akıllı ve hassas tarım teknolojilerinin süs bitkileri sektöründe sürdürülebilir üretim için stratejik bir potansiyel taşıdığı; ancak bunun gerçekleşmesi için ekonomik, teknik ve kurumsal engellerin aşılması gerektiği sonucuna ulaşılmıştır.

🔖

Cite This Article

Bayhan, N. (2025). A Systematic Review on the Applications, Challenges, and Limitations of Smart and Precision Agriculture Technologies in Ornamental Plants. Düzce Üniversitesi Süs ve Tıbbi Bitkiler Botanik Bahçesi Dergisi, Cilt: 4 Sayı: 2, 32–53. https://ajindex.com/articles/1644925

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (67)

  1. Abarna, J., & Selvakumar, A. (2015). Rose flower harvesting robot. International Journal of Applied Engineering, 4216–4222.
  2. Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry - A systematic literature review on agriculture 4.0. Smart Agriculture. Technology, 2, 100042. https://doi.org/10.1016/j.atech.2022.100042 Adli, H. K., Remli, M. A., Wan Salihin Wong, K. N. S., Ismail, N. A., González-Briones, A., Corchado, J. M., & Mohamad, M. S. (2023). Recent advancements and challenges of a IoT application in smart agriculture: A review. Sensors, 23, 3752. https://doi.org/10.3390/s23073752
  3. Ahmed, B., Shabbir, H., Naqvi, S. R., & Peng, L. (2024). Smart agriculture: Current state, opportunities and challenges. Institute of Electrical and Electronics Engineers Access, 12, 144456-144478. https://doi.org/10.1109/ACCESS.2024.3471647
  4. Alahmadi, A. N., Rehman, S. U., Alhazmi, H. S., Glynn, D. G., Shoaib, H., & Solé, P. (2022). Cyber-security threats and side-channel attacks for digital agriculture. Sensors, 22(9), 3520. https://doi.org/10.3390/s22093520
  5. Alves, R. G., Souza, G., Maia, R. F., Tran, A. L. H., Kamienski, C., Soininen, J. P., Aquino, P. T., & Lima, F. (2019). A digital twin for smart farming. In IEEE Global Humanitarian Technology Conference (GHTC), 1-4. https://doi.org/10.1109/GHTC46095.2019.9033075
  6. Anuga, S. W., Gordon, C., Boon, E., & Surugu, J. M. I. (2019). Determinants of climate smart agriculture (CSA) adoption among smallholder food crop farmers in the Techiman Municipality, Ghana. Ghana Journal of Geography, 11(1), 124-139. https://dx.doi.org/10.4314/gjg.v11i1.8
  7. Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. M. (2019). Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. Institute of Electrical and Electronics Engineers Access, 7, 129551–129583. https://doi.org/10.1109/ACCESS.2019.2932609
  8. Balyan, S., Jangir, H., Tripathi, S. N., Tripathi, A., Jhang, T., & Pandey, P. (2024). Seeding a sustainable future: Navigating the digital horizon of smart agriculture. Sustainability, 16(2), 475. https://doi.org/10.3390/su16020475
  9. Banda-Chávez, J. M., Pablo Serrano-Rubio, J., Osvaldo Manjarrez-Carrillo, A., Maria Rodriguez-Vidal, L., & Herrera-Guzman, R. (2018). Intelligent wireless sensor network for ornamental plant care. In Proceedings of the IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, October 2018, https://doi.org/10.1109/IECON.2018.8591644
  10. Bañón, S., Ochoa, J., Bañón, D., Ortuño, M. F., & Sánchez-Blanco, M. J. (2019). Controlling salt flushing using a salinity index obtained by soil dielectric sensors improves the physiological status and quality of potted hydrangea plant. Scientia Horticulturae, 247, 335-343. https://doi.org/10.1016/j.scienta.2018.12.026
  11. Barreto, L., & Amaral, A. (2018). Smart farming: Cyber security challenges. In 2018 International Conference on Intelligent Systems (IS), September, 870-876. https://doi.org/10.1109/IS.2018.8710531
  12. Beeson, R. C., & Brooks, J. (2006). Evaluation of a model based on reference crop evapotranspiration (ETo) for precision irrigation using overhead sprinklers during nursery production of Ligustrum Japonica. V. International Symposium on Irrigation Horticultural Crops, 792, 85–90. https://doi.org/10.17660/ActaHortic.2008.792.7
  13. Bhakta, I., Phadikar, S., & Majumder, K. (2019). State‐of‐the‐art technologies in precision agriculture: A systematic review. Journal of the Science of Food and Agriculture, 99(11), 4878-4888. https://doi.org/10.1002/jsfa.9693
  14. Blasch, J., van der Kroon, B., van Beukering, P., Munster, R., Fabiani, S., Nino, P., & Vanino, S. (2022). Farmer preferences for adopting precision farming technologies: A case study from Italy. European Review of Agricultural Economics, 49(1), 33-81. https://doi.org/10.1093/erae/jbaa031
  15. Cardoso, J. C., & Vendrame, W. A. (2022). Innovation in propagation and cultivation of ornamental plants. Horticulturae, 8(3), 229. https://doi.org/10.3390/horticulturae8030229
  16. Carolan, M. (2020). Automated agri-food futures: Robotics, labor and the distributive politics of digital agriculture. Journal of Peasant Studies, 47(1), 184–207. https://doi.org/10.1080/03066150.2019.1584189
  17. Choi, S. Y., & Lee, A. K. (2020). Development of a cut rose longevity prediction model using thermography and machine learning. Horticultural Science and Technology, 38(5), 675-685. https://doi.org/10.7235/HORT.20200061
  18. Data Bridge Market Research. (2022). Global Flowers and Ornamental Plants Market – Industry Trends and Forecast to 2029. https://www.databridgemarketresearch.com/reports/global-flowers-and-ornamental-plants-market#:~:text=Market%20Analysis%20and%20Size&text=Data%20Bridge%20Market%20Research%20analyses,forecast%20period%20of%202022%2D2029. Erişim Tarihi: 22.08.2025
  19. DGAGRI-G2 (2020). Horticultural products. In Flowers and Ornamental Plants—Production; Statistics 2010–2019; European Commission Working Document. Publications Office of the European Union: Luxembourg, Luxembourg. https://agriculture.ec.europa.eu/system/files/2020-06/flowers-ornamental-plants-statistics_en_0.pdf Erişim Tarihi: 16.08.2025
  20. Eissa, M. (2024). Precision agriculture using artificial intelligence and robotics. Journal of Research in Agriculture and Food Sciences, 1(2), 35-52. https://doi.org/10.5455/JRAFS.20240404014009
  21. Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., et al. (2011). Solutions for a cultivated planet. Nature, 478, 337–342. http://www.nature.com/doifinder/10.1038/nature10452
  22. Food and Agriculture Organization (FAO). (2009). Feeding the world in 2050. https://www.fao.org/4/k6021e/k6021e.pdf Erişim Tarihi: 16.08.2025
  23. Freeman, D., Gupta, S., Smith, D. H., Maja, J. M., Robbins, J., Owen Jr, J. S., Peña, J. M., & Castro, A. I. (2019). Watson on the farm: Using cloud-based artificial intelligence to identify early indicators of water stress. Remote Sensing, 11(22), 2645. https://doi.org/10.3390/rs11222645
  24. Freidenreich, A., Barraza, G., Jayachandran, K., & Khoddamzadeh, A. A. (2019). Precision agriculture application for sustainable nitrogen management of Justicia brandegeana using optical sensor technology. Agriculture, 9(5), 98. https://doi.org/10.3390/agriculture9050098
  25. Gago, J., Douthe, C., Coopman, R. E., Gallego, P. P., Ribas-Carbo, M., Flexas, J., Escalona, J., & Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agriculture Water Managment, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020
  26. Giovannini, A., Laura, M., Nesi, B., Savona, M., & Cardi, T. (2021). Genes and genome editing tools for breeding desirable phenotypes in ornamentals. Plant Cell Reports, 40(3), 461-478. https://doi.org/10.1007/s00299-020-02632-x
  27. Goel, R., & Gupta, P. (2020). Robotics and Industry 4.0. (Ed: Nayyar, A., Kumar, A.), A Road Map to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, Springer: Cham, Switzerland, 157–169. http://dx.doi.org/10.1007/978-3-030-14544-6
  28. Gomiero, T. (2019). Soil and crop management to save food and enhance food security. (Ed: Galanakis, C.M.), Saving Food, Academic Press: Cambridge, MA, USA, 33–87. https://doi.org/10.1016/B978-0-12-815357-4.00002-X
  29. Hendricks, J., Briercliffe, T., Oosterom, B., Treer, A., Kok, G., Edwards, T., & Kong, H. (2019). Productions and Markets, The Future of Ornamentals. AIPH, International Association of Horticultural Producers: Oxfordshire, UK.
  30. Huang, Y., & Wang, Y. (2024). Exploring enhanced object detection and classification methods for Alstroemeria genus morado. International Journal of Advanced Computer Science and Applications, 15(5), 1143-1150. https://doi.org/10.14569/ijacsa.2024.01505116
  31. Huylenbroeck, J. V., & Bhattarai, K. (2022). Ornamental plant breeding: entering a new era?. Ornamental Horticulture, 28(3), 297–305. https://doi.org/10.1590/2447-536X.v28i3.2516
  32. K., M. M. P., Pagariya, M. C., Jadhav, P. R., Gawade, N. S., Sarode, D. K., Karkute, S. G., Kardile, H. B., Deshmukh, R., Penna, S., & Kawar, P.G. (2025). Advancing ornamental plant breeding through genomic technologies: opportunities, challenges, and future directions. Functional and Integrative Genomics, 25(140), 1-23. https://doi.org/10.1007/s10142-025-01640-y
  33. Kashyap, P. K., Kumar, S., Jaiswal, A., Prasad, M., & Gandomi, A. H. (2021). Towards precision agriculture: IoT-Enabled intelligent irrigation systems using deep learning neural network. IEEE Sensors Journal, 21(16), 17479–17491. https://doi.org/10.1109/JSEN.2021.3069266
  34. Kawollek, M., & Rath, T. (2008). Robotic harvest of cut flowers based on image processing by using Gerbera jamesonii as model plant. Acta Horticurae, 801, 557–564. https://doi.org/10.17660/ActaHortic.2008.801.62
  35. Khan, N., Ray, R. L., Zhang, S., Osabuohien, E., & Ihtisham, M. (2022). Influence of mobile phone and internet technology on income of rural farmers: Evidence from Khyber Pakhtunkhwa Province, Pakistan. Technology in Society, 68, 101866. https://doi.org/10.1016/j.techsoc.2022.101866
  36. Kocian, A., Massa, D., Cannazzaro, S., Incrocci, L., Di Lonardo, S., Milazzo, P., & Chessa, S. (2020). Dynamic Bayesian network for crop growth prediction in greenhouses. Computers and Electronics in Agriculture, 169, 105167. https://doi.org/10.1016/j.compag.2019.105167
  37. Li, W., Clark, B., Taylor, J.A., Kendall, H., Jones, G., Li, Z., Jin, S., Zhao, C., Yang, G., Shuai, C., Cheng, X., Chen, J., Yang, H., & Frewer, L. J. (2020). A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems. Computers and Electronics in Agriculture, 172, 105305. https://doi.org/10.1016/j.compag.2020.105305
  38. Li, Y., Luo, J., Liu, Z., Wu, D., & Zhang, C. (2023). A personalized and smart flowerpot enabled by 3D printing and cloud technology for ornamental horticulture. Sensors, 23(13), 6116. https://doi.org/10.3390/s23136116
  39. Liakos V., & Mavridis P. (2019). Sensor networks in precision agriculture. In: Precision agriculture technologies for crop improvement. Springer, Cham, Switzerland, 15–32, 2019.
  40. Lin, Y. B., Chen, Y. T., Hsieh, W. J., Chen, W. L., Lin, Y. W., & Sun, E. (2024). Design of a spore germination sensor for orchids. IEEE Sensors Letters, 9(12). https://doi.org/10.1109/LSENS.2024.3520018
  41. Mahmud, M. S., Zahid, A., Das, A. K. (2023). Sensing and automation technologies for ornamental nursery crop production: Current status and future prospects. Sensors, 23(4), 1818. https://doi.org/10.3390/s23041818
  42. Nasra, P., Singh, J., Rani, S., Shandilya, G., Bharany, S., Sood, S., Rehman, A. U., & Hussen, S. (2025). Optimized ReXNet variants with spatial pyramid pooling, CoordAttention, and convolutional block attention module for money plant disease detection. Discover Sustainability, 6(1), 391. https://doi.org/10.1007/s43621-025-01241-6 Ni, L., Boonsub, P., & Tao, X. (2025). Exploring the bridge between digital transformation and sustainable supply chain performance: An empirical study based on Yunnan fresh cut flower supply chain. Journal of Project Management, 10(2), 185-440. https://doi.org/10.5267/j.jpm.2025.3.002
  43. Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,. & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ, 1-36. https://doi.org/10.1136/bmj.n160
  44. Patiluna, V., Owen Jr. J., Maja, J. M., Neupane, J., Behmann, J., Bohnenkamp, D.,. & de Castro, A. (2025). Using hyperspectral imaging and principal component analysis to detect and monitor water stress in ornamental plants. Remote Sensing, 17(2), 285. https://doi.org/10.3390/rs17020285
  45. Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., & Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. https://doi.org/10.1016/j.compag.2022.107096
  46. Pivoto, D., Barham, B., Waquil, P. D., Foguesatto, C. R., Dalla Corte, V. F., Zhang, D., & Talamini, E. (2019). Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review, 22(4), 571-588. https://doi.org/10.22434/ifamr2018.0086
  47. Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Corte, V. F. D., & Mores, G. D. V. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21–32. https://linkinghub.elsevier.com/retrieve/pii/S2214317316301184
  48. Pratama, H. P., Putri, D. I. H., Putri, H. E., Irawan, E. N., & Kautsar, M. A. R. (2024). Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT. Journal of Mechatronics, Electrical Power, and Vehicular Technology, 15(1), 82-92. https://doi.org/10.55981/j.mev.2024.963
  49. Qi, T. Z. (2023). A review of solar dc microgrids design for smart farming in a New Zealand lifestyle block. 2023 IEEE Fifth International Conference on DC Microgrids (ICDCM), Novemver, 1-5. https://doi.org/10.1109/ICDCM54452.2023.10433636
  50. Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers Sustainable Food Systems, 2, 87. https://doi.org/10.3389/fsufs.2018.00087
  51. Ruett, M., Dalhaus, T., Whitney, C., & Luedeling, E. (2022). Assessing expected utility and profitability to support decision-making for disease control strategies in ornamental heather production. Precision Agriculture, 23(5), 1775-1800. https://doi.org/10.1007/s11119-022-09909-z
  52. Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), 207. https://doi.org/10.3390/agronomy10020207
  53. Shepherd, M., Turner, J.A., Small, B., & Wheeler, D. (2020). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution. Journal of the Science of Food and Agriculture, 100, 5083–5092. https://doi.org/10.1002/jsfa.9346
  54. Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
  55. Süs Bitkileri Üreticileri Alt Birliği (SÜSBİR). (2025). Süs Bitkileri Sektör Raporu. https://www.susbir.org.tr/belgeler/raporlar/susbir-sektor-raporu-2025.pdf#page=2.08 Erişim Tarihi: 22.08.2025
  56. Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002
  57. Traversari, S., Cacini, S., Galieni, A., Nesi, B., Nicastro, N., & Pane, C. (2021). Precision agriculture digital technologies for sustainable fungal disease management of ornamental plants. Sustainability, 13(7), 3707. https://doi.org/10.3390/su13073707
  58. Tsai, Y. H., & Hsu, T. C. (2024). An effective deep neural network in edge computing enabled Internet of Things for plant diseases monitoring. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 695-699. https://doi.org/10.1109/wacvw60836.2024.00081
  59. Ullo, S. L., & Sinha, G. R. (2021). Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sensing, 13(13), 2585. https://doi.org/10.3390/rs13132585
  60. Vecchio, Y., De Rosa, M., Adinolfi, F., Bartoli, L., & Masi, M. (2020). Adoption of precision farming tools: A context-related analysis. Land Use Policy, 94, 104481. https://doi.org/10.1016/j.landusepol.2020.104481
  61. Wani, M. A., Din, A., Nazki, I. T., Rehman, T. U., Al-Khayri, J. M., Jain, S. M.,. & Mushtaq, M. (2023). Navigating the future: exploring technological advancements and emerging trends in the sustainable ornamental industry. Frontiers in Environmental Science, 11, 1188643. https://doi.org/10.3389/fenvs.2023.1188643
  62. Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review Resource Economics, 10, 19–37. https://doi.org/10.1146/annurev-resource-100516-053654
  63. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming—A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
  64. K., M. M. P., Pagariya, M. C., Jadhav, P. R., Gawade, N. S., Sarode, D. K., Karkute, S. G., Kardile, H. B., Deshmukh, R., Penna, S., & Kawar, P.G. (2025). Advancing ornamental plant breeding through genomic technologies: opportunities, challenges, and future directions. Functional and Integrative Genomics, 25(140), 1-23. https://doi.org/10.1007/s10142-025-01640-y
  65. Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,. & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ, 1-36. https://doi.org/10.1136/bmj.n160
  66. Patiluna, V., Owen Jr. J., Maja, J. M., Neupane, J., Behmann, J., Bohnenkamp, D.,. & de Castro, A. (2025). Using hyperspectral imaging and principal component analysis to detect and monitor water stress in ornamental plants. Remote Sensing, 17(2), 285. https://doi.org/10.3390/rs17020285
  67. Wani, M. A., Din, A., Nazki, I. T., Rehman, T. U., Al-Khayri, J. M., Jain, S. M.,. & Mushtaq, M. (2023). Navigating the future: exploring technological advancements and emerging trends in the sustainable ornamental industry. Frontiers in Environmental Science, 11, 1188643. https://doi.org/10.3389/fenvs.2023.1188643