Precision Geolocation of Medicinal Plants - Assessing Machine Learning Algorithms for Accuracy and Efficiency

Aug 5, 2026

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This study investigates the precision geolocation of medicinal plants, linking ecology, conservation, and pharmaceutical research. Machine learning models—gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM)—are evaluated within the CRISP-DM data mining framework to improve accuracy and efficiency. Performance is measured using precision, recall, accuracy, and F1 score. Findings show SVM and GBM achieve the strongest results, reaching 97.29% accuracy, with SVM also delivering notable computational efficiency. RF remains competitive when interpretability is required. The results support improved conservation planning and future pharmaceutical exploration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Background and significance

Prior methods and limitations

Related work in technology and ML

Methods and evaluation

CRISP-DM framework and ML models

Metrics for accuracy and efficiency

Results and discussion

Comparative performance of SVM, GBM, and RF

Interpretability considerations

Advances in Technology Innovation, vol. 9, no. 2, 2024, pp. 85-98 Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency Maria Concepcion Suarez Vera* College of Information and Communications Technology, Catanduanes State University, Catanduanes, Philippines Received 06 February 2024; received in revised form 08 March 2024; accepted 09 March 2024 DOI: [https://doi.org/10.46604/aiti.2024.13355](https://doi.org/10.46604/aiti.2024.13355) Abstract 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. Keywords: geolocation, machine learning, medicinal plants, support vector machine, gradient boosting machine 1. Introduction Tracing back to ancient civilizations and extending into modern ecological conservation and pharmaceutical domains, the precise geolocation of medicinal plants is substantiative regarding the enhancement of healthcare outcomes, preserving biodiversity, and promoting sustainable development. Medicinal plants, integral to the healing traditions of Egyptians, Chinese, Indians, and other cultures, have been perpetually used to prevent, relieve, or treat illnesses. This practice, profoundly embedded in the cultural heritage of numerous communities, has been meticulously documented and passed down through generations. In the Philippines, the melding of Malay, Spanish, and American influences enrich its traditional understanding of medicinal plants, with conventional healers such as “albularyos” or “pilot” using these plants to treat various ailments. Ethnobotanical research in the Philippines highlights the deep traditional knowledge of indigenous tribes, identifying the country as a critical biodiversity hotspot with around 13,000 plant species, 39% of which are endemic [1-2]. This biodiversity underpins the extensive use of 1,500 medicinal plants in traditional medicine, with significant potential recognized for contemporary pharmaceuticals. Among this vast cluster of medicinal plants, 10 plants are widely recognized, 177 are earmarked for further research, and the confirmation of safety and efficacy pertains to 120 plants [3] . These insights emphasize the significance of medicinal plants in both traditional and modern healthcare contexts, showcasing their potential in pharmaceutical development. * Corresponding author. E-mail [address: maconsuarez@gmail.com](address: maconsuarez@gmail.com) [English language proofreader: Chih-Wei Chang](English language proofreader: Chih-Wei Chang) 86 Advances in Technology Innovation, vol. 9, no. 2, 2024, pp. 85-98 The traditional geolocation methods for medicinal plants, including field surveys and basic GPS mapping, have been instrumental yet exhibit a palpable defect in accuracy, efficiency, and data integration, as substantiated in Faizy et al. [4] . Furthermor

FAQs

Which machine learning algorithms are used to assess medicinal plant geolocation in the study?

The study evaluates gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM) to model medicinal plant geolocation performance.

How is model performance measured in the research?

Performance is evaluated using precision, recall, accuracy, and F1 score metrics within the CRISP-DM framework.

What results show SVM’s and GBM’s advantage, and how does RF compare?

SVM and GBM deliver superior performance, with SVM reaching 97.29% accuracy and also demonstrating strong computational efficiency. RF performs worse in accuracy than the top models but remains competitive when interpretability is needed.

Aug 5, 2026

Upload by Noah