Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency
DOI:
https://doi.org/10.46604/aiti.2024.13355Keywords:
geolocation, machine learning, medicinal plants, support vector machine, gradient boosting machineAbstract
This study investigates the precision geolocation of medicinal plants, a critical endeavor bridging ecology, conservation, and pharmaceutical research. By employing machine learning algorithms—gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM)—within the cross-industry standard process for data mining (CRISP-DM) framework, both the accuracy and efficiency of medicinal plant geolocation are enhanced. The assessment employs precision, recall, accuracy, and F1 score performance metrics. Results reveal that SVM and GBM algorithms exhibit superior performance, achieving an accuracy of 97.29%, with SVM showing remarkable computational efficiency. Meanwhile, despite inferior performance, RF remains competitive especially when model interpretability is required. These outcomes highlight the efficacy of SVM and GBM in medicinal plant geolocation and accentuate their potential to advance environmental research, conservation strategies, and pharmaceutical explorations. The study underscores the interdisciplinary significance of accurately geolocating medicinal plants, supporting their conservation for future pharmaceutical innovation and ecological sustainability.
References
C. S. Cordero, U. Meve, and G. J. D. Alejandro, “Ethnobotanical Documentation of Medicinal Plants Used by the Indigenous Panay Bukidnon in Lambunao, Iloilo, Philippines,” Frontiers in Pharmacology, vol. 12, article no. 790567, January 2022.
O. Nuneza, B. Rodriguez, and J. G. Nasiad, “Ethnobotanical Survey of Medicinal Plants Used by the Mamanwa Tribe of Surigao Del Norte and Agusan Del Norte, Mindanao, Philippines,” Biodiversitas Journal of Biological Diversity, vol. 22, no. 6, pp. 3284-3296, June 2021.
J. M. Lopez and J. M. Tram, “Falling Behind and Forgotten: The Impact of Acculturation and Spirituality on the Mental Health Help-Seeking Behavior of Filipinos in the USA,” Asian American Journal of Psychology, vol. 14, no. 2, pp. 218-230, 2023.
H. S. Faizy, G. Y. Haji, S. M. Saeed, T. S. Mala, M. S. Ibrahim, M. A. Khider, et al., “Geographical Study of Medicinal Plants Using GIS and GPS Tools in Some Villages, Barzan Sub-District, Mergasor Districts Iraqi Kurdistan Region,” IOP Conference Series: Earth and Environmental Science, vol. 1252, article no. 012174, December 2023.
L. R. Halpin, J. D. Ross, R. Ramos, R. Mott, N. Carlile, N. Golding, et al., “Double‐Tagging Scores of Seabirds Reveals that Light‐Level Geolocator Accuracy is Limited by Species Idiosyncrasies and Equatorial Solar Profiles,” Methods in Ecology and Evolution, vol. 12, no. 11, pp. 2243-2255, November 2021.
P. A. Singh, A. Sood, and A. Baldi, “An Agro-Ecological Zoning Model Highlighting Potential Growing Areas for Medicinal Plants in Punjab,” Indian Journal of Pharmaceutical Education and Research, vol. 55, no. 2s, pp. s492-s500, April-June 2021.
W. A. R. W. M. Isa, I. M. Amin, and N. Saubiran, “Mobile Application on Malay Medicinal Plants Based on Information Crowdsourcing,” Alinteri Journal of Agriculture Sciences, vol. 36, no. 2, pp. 208-229, 2021.
D. Sugiarto, J. Siswantoro, M. F. Naufal, and B. Idrus, “Mobile Application for Medicinal Plants Recognition from Leaf Image Using Convolutional Neural Network,” Indonesian Journal of Information Systems, vol. 5, no. 2, pp. 43-56, February 2023.
S. Puttinaovarat and P. Horkaew, “A Geospatial Database Management System for the Collection of Medicinal Plants,” Geospatial Health, vol. 16, no. 2, article no. 998, October 2021.
R. Permana, E. T. Tosida, and M. I. Suriansyah, “Development of Augmented Reality Portal for Medicininal Plants Introduction,” International Journal of Global Operations Research, vol. 3, no. 2, pp. 52-63, 2022.
M. O. Faruque, G. Feng, M. N. A. Khan, J. W. Barlow, U. R. Ankhi, S. Hu, et al., “Qualitative and Quantitative Ethnobotanical Study of the Pangkhua Community in Bilaichari Upazilla, Rangamati District, Bangladesh,” Journal of Ethnobiology and Ethnomedicine, vol. 15, article no. 8, 2019.
P. Boycheva and D. Ivanov, “Comparative Ethnobotanical Analysis of the Used Medicinal Plants in the Region of the Northern Black Sea Coast (Bulgaria),” Acta Scientifica Naturalis, vol. 8, no. 2, pp. 44-54, July 2021.
K. S. M. Anbananthen, S. Subbiah, D. Chelliah, P. Sivakumar, V. Somasundaram, K. H. Velshankar, et al., “An Intelligent Decision Support System for Crop Yield Prediction Using Hybrid Machine Learning Algorithms,” F1000Research, vol. 10, article no. 1143, November 2021.
Y. M. Chen, Y. Kao, C. C. Hsu, C. J. Chen, Y. Ma, Y. S. Shen, et al., “Real‐Time Interactive Artificial Intelligence of Things-Based Prediction for Adverse Outcomes in Adult Patients with Pneumonia in the Emergency Department,” Academic Emergency Medicine, vol. 28, no. 11, pp. 1277-1285, November 2021.
P. Theerthagiri and J. Vidya, “Cardiovascular Disease Prediction Using Recursive Feature Elimination and Gradient Boosting Classification Techniques,” Expert Systems, vol. 39, no. 9, article no. e13064, November 2022.
C. A. ul Hassan, J. Iqbal, S. Hussain, H. AlSalman, M. A. A. Mosleh, and S. Sajid Ullah, “A Computational Intelligence Approach for Predicting Medical Insurance Cost,” Mathematical Problems in Engineering, vol. 2021, article no. 1162553, 2021.
S. Mohapatra and N. Chaudhary, “Statistical Analysis and Evaluation of Feature Selection Techniques and Implementing Machine Learning Algorithms to Predict the Crop Yield Using Accuracy Metrics,” Engineered Science, vol. 21, article no. 787, February 2023.
M. Waleed, T. W. Um, T. Kamal, and S. M. Usman, “Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things,” Symmetry, vol. 13, no. 3, article no. 403, March 2021.
H. H. Wang, C. C. Huang, P. C. Talley, and K. M. Kuo, “Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls,” Journal of Healthcare Engineering, vol. 2022, article no. 3100618, 2022.
P. Michailidis, A. Dimitriadou, T. Papadimitriou, and P. Gogas, “Forecasting Hospital Readmissions with Machine Learning,” Healthcare, vol. 10, no. 6, article no. 981, June 2022.
H. Oh, “A YouTube Spam Comments Detection Scheme Using Cascaded Ensemble Machine Learning Model,” IEEE Access, vol. 9, pp. 144121-144128, 2021.
D. J. Pangarkar, R. Sharma, A. Sharma, and M. Sharma, “Assessment of the Different Machine Learning Models for Prediction of Cluster Bean (Cyamopsis Tetragonoloba L. Taub.) Yield,” Advances in Research, vol. 21, no. 9, pp. 98-105, 2020.
C. Sawangwong, K. Puangsuwan, N. Boonnam, S. Kajornkasirat, and W. Srisang, “Classification Technique for Real-Time Emotion Detection Using Machine Learning Models,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 4, pp. 1478-1486, December 2022.
L. Shi, Y. Qin, J. Zhang, Y. Wang, H. Qiao, and H. Si, “Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection,” Journal of Information Technology Research, vol. 15, no. 1, pp. 1-17, 2022.
T. Suresh, T. A. Assegie, S. Rajkumar, and N. K. Kumar, “A Hybrid Approach to Medical Decision-Making: Diagnosis of Heart Disease with Machine-Learning Model,” International Journal of Electrical and Computer Engineering, vol. 12, no. 2, pp. 1831-1838, April 2022.
F. Martinez-Plumed, L. Contreras-Ochando, C. Ferri, J. Hernandez-Orallo, M. Kull, N. Lachiche, et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3048-3061, August 2021.
J. S. Saltz and I. Krasteva, “Current Approaches for Executing Big Data Science Projects-A Systematic Literature Review,” PeerJ Computer Science, vol. 8, article no. e862, 2022.
D. Oliveira, D. Ferreira, N. Abreu, P. Leuschner, A. Abelha, and J. Machado, “Prediction of COVID-19 Diagnosis Based on OpenEHR Artefacts,” Scientific Reports, vol. 12, article no. 12549, 2022.
S. Montaha, S. Azam, A. K. M. R. H. Rafid, S. Islam, P. Ghosh, and M. Jonkman, “A Shallow Deep Learning Approach to Classify Skin Cancer Using Down-Scaling Method to Minimize Time and Space Complexity,” PLoS ONE, vol. 17, no. 8, article no. e0269826, 2022.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Maria Concepcion Suarez Vera
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. is accepted for publication. Authors can retain copyright of their article with no restrictions.
Since Jan. 01, 2019, AITI will publish new articles with Creative Commons Attribution Non-Commercial License, under The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
