Evidence map›Paper›PMID 41784667›Full record

ReviewPlanta2026

Smart farming approaches in medicinal plant cultivation: a review of techniques, benefits, and sustainability.

Sanjeev Khan, Nutan Pathania, Pawan Kumar, Rahul Kumar, Jitender Kumar, Nitesh Kumar, Arti Sharma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Planta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sanjeev KhanDepartment of Data Science and AI, Himachal Pradesh University, Himachal Pradesh, Shimla, 171005, India.
Nutan PathaniaDepartment of Computer Science, Himachal Pradesh University, Himachal Pradesh, Shimla, 171005, India.
Pawan KumarDepartment of Computer Science, Himachal Pradesh University, Himachal Pradesh, Shimla, 171005, India.
Rahul KumarFaculty of Agricultural Sciences, DAV University, Sarmastpur, Jalandhar, Punjab, 144001, India.
Jitender KumarDepartment of Plant Sciences, Central University of Himachal Pradesh, Shahpur, Kangra, 176206, India.
Nitesh KumarDepartment of Biosciences, Himachal Pradesh University, Shimla, Himachal Pradesh, 171005, India. niteshchauhan7@gmail.com.ORCID http://orcid.org/0000-0001-5115-2029
Arti SharmaFaculty of Agriculture, Abhilashi University, Mandi, Himachal Pradesh, 175045, India. artijamwalsharma2016@gmail.com.ORCID http://orcid.org/0000-0001-8438-5451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MAIN

conclusionSmart farming technologies significantly enhance medicinal plant cultivation by improving yield, quality, and sustainability, while addressing traditional challenges through precision, automation, and data-driven decision-making. Medicinal plants have played a vital role in healthcare and the pharmaceutical industry. However, traditional cultivation methods face challenges, such as variable yield due to environmental stress and suboptimal resource use. While pharmacopeias already define strict quality parameters for medicinal plant material, smart farming technologies can further support consistency, sustainability, and efficiency in cultivation. This review critically examines the integration of smart farming technologies to optimize the biological mechanisms governing the growth and phytochemical production of medicinal plants. This paper focuses on key physiological processes including photosynthesis regulation, nutrient uptake, stress response, and secondary metabolite biosynthesis, which are directly influenced by precision irrigation, AI-driven nutrient management, and controlled-environment agriculture. Countries such as the Netherlands (80%), Japan (75%), and the USA (70%) are leading adopters, using automated greenhouses, artificial intelligence crop analytics, and drones. Key medicinal crops benefiting include Withania somnifera (L.) Dunal, Panax ginseng Makino, Echinacea purpurea (L.) Moench, Lavandula angustifolia Mill., Ocimum sanctum L., Hypericum perforatum L., Cinnamomum verum J.Presl, and Coriandrum sativum L. Techniques, such as precision irrigation, soil health monitoring, artificial intelligence-based pest detection, controlled-environment agriculture, and drone surveillance, have shown major improvements. Empirical studies report improvements in water efficiency and phytochemical yields in these plants, with the results derived from empirical trials conducted in controlled settings. However, scalability and economic feasibility of these technologies in diverse climatic regions remain challenges. Despite these gains, barriers like high costs, limited tech literacy, infrastructure gaps, and regulatory hurdles remain. Addressing these through funding, education, and policy change is essential. Future integration of genomics and metabolomics could further boost yield, quality, and sustainability. This review advances the field by providing a comprehensive framework for adopting smart farming in medicinal plant cultivation, linking technology trends with practical outcomes and global adoption insights.

Indexed as

AgriculturePlants, MedicinalArtificial IntelligenceCrops, AgriculturalArtificial intelligenceGenomicsMedicinal plant cultivationSmart farming technologiesSustainability

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.