A Comprehensive Literature Review on Emerging Potentials of Machine Learning Algorithms on Geospatial Platform for Medicinal Plant Cultivation Management in Existing Scenario

Medicinal plants have been an essential part of the traditional medicine systems of India over the centuries. Medicinal plants are used in different industries such as healthcare, pharmaceuticals, food, cosmetics, and many more. The global market of plants based agriculture is US $ 32.702 billion and that of Asia is US $ 14.505 billion. With 8.75% trade, India holds the 2nd position in export of medicinal plants in Asian trade. With the growing demand for natural remedies and the need for sustainable agricultural practices, the cultivation of medicinal plants has gained significant attention. To ensure optimal plant growth and bioactive compound production, effective management of medicinal plant cultivation is essential. In spite of having huge demand for medicinal plants in the global market, it has several issues such as process standardizations, lack of adapting new age technology, minimum support price, in-consistent supply of medicinal plants, lack of market linkages, underdeveloped cultivation technology, poor awareness for conservation of species, and many more. The machine learning approach has shown great potential in various fields, including agriculture. It can analyze complex data from various sources such as soil composition, climate data, and plant health indicators. Machine learning approach has various advantages such as predictive analysis, recommendation engine, data-driven decision-making, precision farming, optimal resource management, crop monitoring, yield prediction, cultivar selection, effective harvest planning, knowledge sharing, etc. Integrating data-driven decision-making with traditional agricultural practices paves the way for a more sustainable and efficient approach to meet the growing demand for medicinal plants while preserving biodiversity and ecosystem health. The study demonstrates the potential of machine learning techniques on geospatial platforms for sustainable medicinal plant cultivation management. Along with this, the research paper tries to recommend a suitable machine learning framework that can be effectively used during the medicinal plant cultivation processes.