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Hatami, Shahnaz, Hatami Badrbani, Mohammad, Kahrizi, Danial. (1403). Data Mining Approach in the Agricultural Industry, Medicinal Plants (case study) A Review. سامانه مدیریت نشریات علمی, 13(2), 247-256. doi: 10.22034/jmpb.2024.364419.1636
Shahnaz Hatami; Mohammad Hatami Badrbani; Danial Kahrizi. "Data Mining Approach in the Agricultural Industry, Medicinal Plants (case study) A Review". سامانه مدیریت نشریات علمی, 13, 2, 1403, 247-256. doi: 10.22034/jmpb.2024.364419.1636
Hatami, Shahnaz, Hatami Badrbani, Mohammad, Kahrizi, Danial. (1403). 'Data Mining Approach in the Agricultural Industry, Medicinal Plants (case study) A Review', سامانه مدیریت نشریات علمی, 13(2), pp. 247-256. doi: 10.22034/jmpb.2024.364419.1636
Hatami, Shahnaz, Hatami Badrbani, Mohammad, Kahrizi, Danial. Data Mining Approach in the Agricultural Industry, Medicinal Plants (case study) A Review. سامانه مدیریت نشریات علمی, 1403; 13(2): 247-256. doi: 10.22034/jmpb.2024.364419.1636

Data Mining Approach in the Agricultural Industry, Medicinal Plants (case study) A Review

Journal of Medicinal plants and By-products
مقاله 1، دوره 13، شماره 2، شهریور 2024، صفحه 247-256    اصل مقاله (960.01 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/jmpb.2024.364419.1636
نویسندگان
Shahnaz Hatami* 1؛ Mohammad Hatami Badrbani2؛ Danial Kahrizi3
1Department of Industrial Engineering, Faculty of Industrial Engineering, System Management and Productivity, Arak Branch, Islamic Azad University, Arak, Iran
2Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Alborz, Iran
3Agricultural Biotechnology Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
چکیده
In the realm of agriculture and natural resources, medicinal plants stand out as a valuable resource. In recent years, faced with challenges such as predicting climate changes, soil classification, land use, and identifying patterns, there is a growing need for optimal techniques with higher efficiency, particularly in the cultivation of medicinal plants. Therefore, this article introduces the application of data mining to analyze available data in the agriculture and natural resources areas, focusing specifically on the medicinal plant industry. The primary objective is to explore data mining techniques that can enhance various aspects of medicinal plant cultivation, addressing challenges related to climate predictions, soil classification, and optimizing production. The article concludes by presenting the most effective data analysis methods in this domain, accompanied by their corresponding algorithms. Additionally, the aforementioned research is a guide for those intending to investigate the applications of data mining methods are highlighted for increased productivity, encompassing areas such as predicting crop yield, forecasting weather conditions, rainfall patterns, seed and plant conditions, soil quality, and medicinal plant production. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
کلیدواژه‌ها
Data mining؛ Medicinal plants؛ Classification؛ Clustering
مراجع
  1. Chen S.L., Yu H., Luo H.M., Wu Q., Li C.F. A Steinmetz. Conservation and sustainable use of medicinal plants: Problems, progress, and prospects. Chin. Med. 2016; 11.
  2. He J., Yang B., M. Dong, et al. Crossing the roof of the world: Trade in medicinal plants from Nepal to China. J. Ethnopharmacol., 224. 2018;100-110.
  3. Shen T., Yu H., Wang Y. Assessing the impacts of climate change and habitat suitability on the distribution and quality of medicinal plant using multiple information integration: Take Gentiana rigescens as an example. Ecol. Indic. 2021.; 123.
  4. Hamilton A.C. Medicinal plants, conservation and livelihoods. Biodiversity & Conservation. 2004; 13(8), 1477–1517. https://doi.org/10.1023/B:BIOC.0000021333.23413.42
  5. Molly Meri Robinson Robinson, Xiaorui Zhang. The World Medicines Situation 2011 Traditional Medicines: Global Situation, Issues and Challenges. World Health Organization, Geneva. 1-2. 2011.
  6. Jean-Marc Fromentin, Marla R. Emery, John Donaldson, Marie-Claire Danner, Agnès Hallosserie, Daniel Kieling. Thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany. 2022. https://doi.org/10.5281/zenodo.6448567
  7. Smith T., Majid F., Eckl V., Morton Reynolds C. Herbal supplement sales in US increase by record-breaking 17.3% in 2020. Herbal Gram. 2021; 131: 52–65.
  8. Timoshyna A., Ke Z., Yang Y., Ling X., Leaman D. The invisible trade: Wild plants and you in the time of COVID-19. Traffic International. 2020.
  9. WHO. WHO global report on traditional and complementary medicine 2019. World Health Organization, Geneva, Switzerland. WHO. 2019.
  10. Industry Research Biz. Global herbal medicine market size, manufacturers, supply chain, sales channel and clients. IRB. 2020–2028.
  11. Vasisht K., Sharma N., Karan M. Current perspective in the international trade of medicinal plants material: An update. Current Pharmaceutical Design. 2016; 22(27): 4288–4336. https://doi.org/10.2174/1381612822666160607070736
  12. Kumar A., Kumar S., Komal R.N., Singh P. Role of traditional ethnobotanical knowledge and indigenous communities in achieving sustainable development goals. Sustainability. 2021; 13(6): 3062. https://doi.org/10.3390/su13063062
  13. Nord J.H., Nord G.D. MIS research: Journal status and analysis. Information & Management. 1995; 29: 29–42.
  14. Osinga S., Paudel D., Mouzakitis S., Athanasiadis I. Big data in agriculture: Between opportunity and solution. Agricultural Systems. 2022; 195: 103298. https://doi.org/10.1016/j.agsy.2021.103298
  15. Rosenblatt P., Lasley P. Perspective on Farm Accident Statistics. The J. Rural Health. 2008; 7: 51–62. https://doi.org/10.1111/j.1748-0361.1991.tb00703.x
  16. Kamble S., Gunasekaran A., Gawankar S. Achieving Sustainable Performance in a Data-driven Agriculture Supply Chain: A Review for Research and Applications. International Journal of Production Economics. 2019; 219: 179–194. https://doi.org/10.1016/j.ijpe.2019.05.022
  17. Chavas J.-P., Chambers R., Pope R. Production Economics and Farm Management: A Century of Contributions. American Journal of Agricultural Economics. 2010; 92: 356–375. https://doi.org/10.1093/ajae/aaq004
  18. Khate A., Sharma B. Medicinal Plant Classification Using Neural Network. 2023; 297–307. https://doi.org/10.1007/978-981-99-4362-3_28
  19. Pešić M. Development of natural product drugs in a sustainable manner. Brief for United Nations Global Sustainable Development Report 2015. Available at: https://sustainabledevelopment.un.org/content/documents/6544118_Pesic_Development%20of%20natural%20product%20drugs%20in%20a%20%20sustainable%20manner.pdf. (Accessed August 15, 2018).
  20. Cordell G.A. Sustainable medicines and global health care. Planta Med. 2011; 77(11):1129-38. doi: 10.1055/s-0030-1270731. Epub 2011 Feb 9. PMID: 21308611.
  21. Applequist W., Brinckmann J.A., Cunningham A., Hart R., Heinrich M., Katerere D., Andel T. Scientists’ Warning on Climate Change and Medicinal Plants. Planta Medica. 2019; 86. https://doi.org/10.1055/a-1041-3406
  22. He M., Wu C., Li L., Zheng L., Tian T., Jiang L., Li Y., Teng F. Effects of Cavitation Jet Treatment on the Structure and Emulsification Properties of Oxidized Soy Protein Isolate. In Foods. 2021; 10(1). https://doi.org/10.3390/foods10010002
  23. Fitzgerald M., Heinrich M., Booker A. Medicinal Plant Analysis: A Historical and Regional Discussion of Emergent Complex Techniques. Frontiers in Pharmacology. 2020; 10: 1480. https://doi.org/10.3389/fphar.2019.01480
  24. Wang P., Yu Z. Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy: A review. Journal of Pharmaceutical Analysis. 2015; 46. https://doi.org/10.1016/j.jpha.2015.04.001
  25. Sanaeifar A., Li X., He Y., Huang Z., Zhan Z. A data fusion approach on confocal Raman microspectroscopy and electronic nose for quantitative evaluation of pesticide residue in tea. Biosystems Engineering. 2021; 210: 206–222. https://doi.org/https://doi.org/10.1016/j.biosystemseng.2021.08.016
  26. Azcarate S.M., Ríos-Reina R., Amigo J.M., Goicoechea H.C. Data handling in data fusion: Methodologies and applications. TrAC Trends in Analytical Chemistry. 2021; 143: 116355. https://doi.org/https://doi.org/10.1016/j.trac.2021.116355
  27. Zhang P., Li T., Yuan Z., Luo C., Wang G., Liu J., Du S. A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data. Information Fusion. 2022;80:87–103. https://doi.org/https://doi.org/10.1016/j.inffus.2021.10.017
  28. Zhou X., Li X., Zhao B., Chen X., Zhang Q. Discriminant analysis of vegetable oils by thermogravimetric-gas chromatography/mass spectrometry combined with data fusion and chemometrics without sample pretreatment. LWT. 2022; 161: 113403. https://doi.org/https://doi.org/10.1016/j.lwt.2022.113403
  29. Hariharan U., Kotteswaran R., Pathak N. The Convergence of IoT with Big Data and Cloud Computin. 2020: 1–23. https://doi.org/10.1201/9781003054115-1
  30. Khan N., Yaqoob I., Hashem I.A.T., Inayat Z., Mahmoud Ali W.K., Alam M., Shiraz M., Gani A. Big Data: Survey, Technologies, Opportunities, and Challenges. Scientific World J. 2014.
  31. Galicia J.F., Torres F., Martínez-Álvarez Troncoso A. A novel spark-based multi-step forecasting algorithm for big data time series. Inf. Sci. 2018; 467: 800-818.
  32. Aggarwal C.C. Data Mining: The Textbook. Springer Publishing Company, Incorporated. 2015.
  33. Veronique Bellon-Maurel, Ludovic Brossard, Frédérick Garcia, Nathalie Mitton, Termier Alexandre. Getting the Most Out of Digital Technology to Contribute to the Transition to. Sustainable Agriculture and Food Systems. Université de Rennes. 2022.
  34. https://www.n-ix.com/big-data-in-agriculture/
  35. (talend.com/resources/big-data-agriculture).
  36. Majumdar J., Naraseeyappa S., Ankalaki S. Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data. 2017; 4(1): 20. https://doi.org/10.1186/s40537-017-0077-4
  37. Liao S.-H., Chu P.H., Hsiao pei-yuan. Review: Data mining techniques and applications - A decade review from 2000 to 2011. Expert Systems with Applications: An International J. 2012; 39: 11303–11311. https://doi.org/10.1016/j.eswa.2012.02.063
  38. Eskandari S., Ravanbakhsh H., Ahangaran Y., Rezapour Z., Pourghasemi H. Effect of climate change on fire regimes in natural resources of northern Iran: investigation of spatiotemporal relationships using regression and data mining models. Natural Hazards. 2023; 119: 1–25. https://doi.org/10.1007/s11069-023-06133-4
  39. Olson D. L. Data Mining BT - Encyclopedia of Optimization (C. A. Floudas & P. M. Pardalos (eds.). 2009; 600–607. Springer US. https://doi.org/10.1007/978-0-387-74759-0_108
  40. Attewell P., Monaghan D.B., Kwong D. Data Mining for the Social Sciences (1st ed.). University of California Press. 2015. http://www.jstor.org/stable/10.1525/j.ctt13x1gcg
  41. Hassani H., Saporta G., Silva E. Data Mining and Official Statistics: The Past, the Present and the Future. Big Data. 2014; 2: 34–43. https://doi.org/10.1089/big.2013.0038
  42. Mulik S., More A. Future of Agriculture with Data Mining. 2023; 47: 32–39.
  43. Aishwarya K., Jabbar M.A. Data Mining Analysis for Precision Agriculture: A Comprehensive Survey. ECS Transactions. 2022; 107: 17769–17781. https://doi.org/10.1149/10701.17769ecst
  44. Bhagawati K., Sen A., Shukla K.K., Bhagawati R. Application and Scope of Data Mining in Agriculture. International Journal of Advanced Engineering Research and Science. 2016; 3(7).
  45. Milovic B., Radojević V. Application of data mining in agriculture. 2015; 21: 26–34.
  46. Coleman S. A Practical Guide to Data Mining for Business and Industry. 2014. https://doi.org/10.1002/9781118763704
  47. Plotnikova V., Dumas M., Fredrik P. Milani. Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements. Data & Knowledge Engineering. 2022; 139; 102013.
  48. Zhang Y., Wang Y. Recent trends of machine learning applied to multi-source data of medicinal plants. Journal of Pharmaceutical Analysis. 2023. https://doi.org/https://doi.org/10.1016/j.jpha.2023.07.012
  49. Taoufik N., Boumya W., Achak M., et al. The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning. Sci. Total Environ. 2022; 807. https://doi.org/10.1016/j.scitotenv.2021.150554.
  50. D.V. Nazarenko, P.V. Kharyuk, I.V. Oseledets, I.A. Rodin, O.A. Shpigun. Machine learning for LC–MS medicinal plants identification. Chemom. Intell. Lab. Syst., 156 (2016), pp. 174-180. https://doi.org/10.1016/j.chemolab.2016.06.003
  51. Meng T., Jing X., Yan Zh., Pedrycz W. A survey on machine learning for data fusion. Inf. Fusion. 2020; 57: 115-129.
  52. Magnus M., Virte H. Thienpont, L. Smeesters. Combining optical spectroscopy and machine learning to improve food classification. Food Contr. 2021; 130.
  53. Krishnamoorthy M., Karthikeyan R. Competence of medicinal plant database using data mining algorithms for large biological databases. Measurement: Sensors. 2022;24:100420. https://doi.org/https://doi.org/10.1016/j.measen.2022.100420
  54. Mucherino A., Papajorgji P., Pardalos P. Data Mining in Agriculture. 2009; 34. https://doi.org/10.1007/978-0-387-88615-2
  55. Beniwal S., Arora J. Classification and feature selection techniques in data mining. Inter J Engin. Res. and Technol. 2012;1.
  56. Patel D., Modi R., Sarvakar K. A Comparative Study of Clustering Data Mining: Techniques and Res Challenges. 2014. https://doi.org/10.13140/2.1.1508.7042
  57. Xu R., Wunsch D. Survey of Clustering Algorithms. Neural Networks, IEEE Transactions On. 2005; 16: 645–678. https://doi.org/10.1109/TNN.2005.845141
  58. Megala S., Hemalatha D. A Novel Datamining Approach to Determine the Vanished Agricultural Land in Tamilnadu. International Journal of Computer Applications. 2011; 23. https://doi.org/10.5120/2869-3718
  59. Rainsford C., Roddick J. Database Issues in Knowledge Discovery and Data Mining. Australasian J. of Inf. Systems. 1999; 6. https://doi.org/10.3127/ajis.v6i2.310
  60. Yavuz E., Şahin M. Semiparametric Regression Models and Applicability in Agriculture. 2022; 5; 160–166. https://doi.org/10.47115/bsagriculture.1077101
  61. Chen M.-S., Han J., Yu P. Data mining: An overview from a database perspective. Knowledge and Data Engineering, IEEE Transactions On. 1997; 8: 866–883. https://doi.org/10.1109/69.553155
  62. Ait Issad H., Aoudjit R., Rodrigues J.J.P.C. A comprehensive review of Data Mining techniques in smart agriculture. Engineering in Agriculture, Environment and Food. 2019; 12(4): 511–525. https://doi.org/https://doi.org/10.1016/j.eaef.2019.11.003.
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