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

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Industrial Engineering, System Management and Productivity, Arak Branch, Islamic Azad University, Arak, Iran

2 Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Alborz, Iran

3 Agricultural Biotechnology Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

Abstract

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.

Keywords

Main Subjects

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Journal of Medicinal plants and By-Products
Volume 13, Issue 2
June 2024
Pages 247-256

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