SJA_42_3_1246-1263

Introduction

Presently, medicinal and aromatic plants (MAPs) have become a vital segment of the global botanical economy due to their diverse uses in pharmaceutical, nutraceutical, cosmetic, pesticidal, and food industries (Singh et al., 2024; Ahmed et al., 2025; Zamani et al, 2025). In this modern period, approximately 80% of the world’s population is dependent on traditional plant-based remedies for primary healthcare (WHO, 2023). The ever-increasing demand for natural therapeutics, plant-derived ingredients, and ecological sustainability has significantly expanded the scope of medicinal and aromatic plants across the globe. However, cultivation of such medicinal plants continuously faces challenges such as undefined agronomic practices, suboptimal input application, quality inconsistency, and harsh environmental conditions (Singh et al., 2021; Pandey et al., 2023).

Fluctuations in yield and the accumulation of secondary metabolites of medicinal plants have been observed due to traditional agronomic practices (Alami et al., 2024). These fluctuations are mainly characterized by differences in soil fertility, climatic conditions, and cultural practices across the globe. Due to suboptimal cultivation protocols, not only does it limit the productivity, but also directly compromises the pharmacological uniformity and commercial reliability of MAP-derived products and indirectly affects quality (Yousefzadeh et al., 2022). Modern techniques have become unavoidable for the production of medicinal and aromatic plants on a sustainable basis, due to a massive increase in demand (Marcelino et al., 2023; Chen, 2025; Mahato et al., 2025).

The idea of precision agriculture (PA), also termed site-specific crop management, presents a modern strategy to enhance the efficiency of available resources on long-term sustainability for medicinal and aromatic plant (MAP) cultivation. This technique integrates digital and automated tools such as remote sensing, global positioning systems (GPS), unmanned aerial vehicles (UAVs), geographic information systems (GIS), Internet of Things (IoT) sensors, and artificial intelligence (AI) to continuously observe field conditions and guide the application of inputs (Pandit et al., 2026). Through real-time data collection and analysis, PA supports informed decision-making that improves the effectiveness of fertilizers and irrigation, stabilizes crop yield and quality, and reduces adverse environmental impacts (Gebbers and Adamchuk, 2010; Jawad et al., 2022; Zhou et al., 2023; Kumar et al., 2025).

Positive response of medicinal and aromatic plants (MAPs) has been recorded when grown under controlled and precision agriculture practices, as their growth and secondary metabolite accumulations are highly influenced by microclimatic shifts as well as soil fertility dynamics (Jampílek and Kráľová, 2023). Water-use efficacy can be improved through the deployment of smart irrigation systems equipped with soil moisture sensors, as done in drought-susceptible species such as Withania somnifera. Likewise, the application of spectral or hyperspectral imaging technologies enables early identification of nutrient imbalances and pest infestations in genera such as Ocimum, Mentha, and Lavandula (Kour and Rana, 2020; Rana et al., 2022). In addition, machine-learning and artificial-intelligence-driven predictive models can help determine the optimal harvest stage to maximize essential oil concentration and other key phytochemicals, thereby improving both biochemical yield and market value (Patel et al., 2021; Varghese et al., 2025).

Despite these benefits, there is limited integration of precision agriculture in MAP cultivation. Major limiting factors include high initial costs, lack of infrastructure in rural production zones, limited digital literacy among farmers, technical staff, and insufficient species-specific calibration models for PA tools (Dajic-Stevanovic and Pljevljakusic, 2015; Aulakh and Malhi, 2021). Additionally, there is a scarcity of consolidated research reviewing the applicability, benefits, and bottlenecks of precision agriculture in the context of MAPs.

Therefore, this review aims to critically study the current landscape of precision agriculture approaches as applied to medicinal and aromatic plant production. It explores the technological tools available, evaluates field-based evidence, highlights specific needs and challenges linked with MAPs (Ahmadi et al., 2025), and proposes future directions for technology integration. This synthesis seeks to provide a foundational perspective for researchers, agronomists, and policymakers interested in improving performance, sustainability, and quality of MAP cultivation through precision agriculture.

Overview of precision agriculture

Precision agriculture is a science-based strategy to farm management that highlights site-specific interventions to optimize crop productivity while efficiently utilizing inputs and being sustainable and eco-friendly. PA integrates real-time data collection, spatial variability analysis, and automated decision making; it provides a chance to transform farming systems through greater resource efficiency and sustainability (Gebbers and Adamchuk, 2010; Getahun et al., 2024). Although its application is well-established in cereals and other cash crops, the incorporation of PA approaches into the cultivation of medicinal and aromatic plants (MAPs) is still at an initial stage (Hernández-Bolaños et al., 2025).

Basically, MAPs differ from conventional food crops in their production objectives. The focus in MAP cultivation lies in the biosynthesis of bioactive constituents such as essential oils, alkaloids, flavonoids, and terpenoids rather than in biomass yield. These secondary metabolites are highly sensitive to biotic and abiotic stresses, including soil fertility, water availability, microclimate conditions, and plant protection measures. Slight diversity in agronomic practices can notably change the concentration and composition of important bioactive compounds (Yousefzadeh et al., 2022; Pandey et al., 2023; Arenas-Salazar et al., 2024). Consequently, optimized environmental conditions and good agronomic practices are imperative to safeguard quality assurance, especially for plants used in pharmacopoeias and certified herbal products.

Various techniques are being used for precision agriculture in medicinal and aromatic plant cultivation. Multispectral imaging from UAVs or satellites is a prominent example of remote sensing used to detect initial physiological stress, disease outbreaks, and drought, assessed through vegetation indices. These techniques offer assessment of crop performance on a large scale and non-destructively, with advanced decision of irrigation and nutrient application (Rana et al., 2022). Furthermore, IoT soil sensors offer nonstop real-time data on parameters like pH of the soil, moisture, and salinity level, which are necessary for plants such as Ocimum sanctum, Mentha arvensis, and W. somnifera, to maintain quality and yield of essential phytochemicals (Prasanna et al., 2024).

Field variations can be assessed by the growers through Geospatial mapping using GPS and GIS technologies. This valuable information enables application of variable rate technologies (VRT), where seeds, fertilizers, and pesticides are applied only where and when needed. These techniques are environmentally friendly and promote uniform plant growth and development, along with constant phytochemical characteristics that are vital for maintaining the therapeutic reliability of medicinal plants (Sharma et al., 2023). Moreover, a blend of environmental modeling with artificial intelligence and machine learning algorithms can forecast pest occurrences or harvest indices for optimal phytochemicals accumulation (Kour and Rana, 2020; Jawad et al., 2022; Maryam et al., 2025).

Although these techniques have a notable impact on the cultivation of MAP but still, due to certain limitations like lack of awareness among MAP growers, the absence of species-specific calibration data for sensors, inadequate digital infrastructure in distant cultivated areas and high initial investment, restricts its application. Furthermore, since MAPs are often cultivated in marginal environments by smallholder farmers, adoption of high-tech approaches requires context-specific adaptation and capacity-building interventions (Aulakh and Malhi, 2021). In conclusion, while precision agriculture holds considerable promise for improving the yield and quality of medicinal and aromatic plants, its implementation must be guided by localized research, cost-effective tools, and participatory technology transfer.

Relevance of precision agriculture in MAP cultivation

Medicinal and aromatic plants (MAPs) play a vital role in both traditional and modern healthcare systems, as well as in pharmaceutical, cosmetic, and food industries. Globally, more than 53,000 species of plants are being used for medicinal purposes, and approximately 3,000 species are traded on a commercial level, with a market growth rate of 6-8% annually (WHO, 2023; Zamani et al., 2025). India is one of the major contributors to the supply chain of medicinal and aromatic plants, as it shares around 7,000 species, out of which approximately 1,200 are commercially important, with popular crops including W. somnifera, O. sanctum, M. arvensis, A. vera, and R. damascena (NMPB, 2022; Ahmed et al., 2025). Apart from their importance, production systems for most MAPs remain suboptimal, often forced by poor agronomic practices, fragmented cultivation areas, low productivity, and fluctuations in the quality of phytochemicals.

Precision agricultural interventions are adoptable for the production of medicinal and aromatic plants due to their inherent sensitivity to environmental variables. In contrast to food crops, where yield is measured in terms of biomass, medicinal plants are valued for their ability to accumulate phytochemical compounds. Quantification and quality of phytochemical profile such as alkaloids, essential oils, glycosides, and phenolics in medicinal plants, are mainly influenced by local environmental and agronomic practices, including maintaining soil fertility, pest control, and water availability to medicinal plants (Yousefzadeh et al., 2022). Notable variation was observed in menthol contents in Mentha species in response to various levels of nitrogen and irrigation (Rathi et al., 2020). Similarly, linalool and eugenol contents in Ocimum basilicum were affected by plant population, light duration, and nutrient status of soil (Saxena et al., 2021). These variables highlight the significance of precision agriculture approaches that enable real-time observing and targeted management of these growth-determining factors.

Modern approaches like remote sensing, Internet of Things (IoT)-based soil and crop sensors, and variable rate input systems, provide continuous and site-specific data that help real-time management of medicinal plant fields. In aromatic crops like R. damascena, thermal imaging through unmanned aerial vehicles (UAVs) has been efficiently utilized to sense drought and finalize irrigation schedules, resulting in improved flower yield and higher essential oil recovery (Bakhshi et al., 2022). Similarly, the application of sensor-guided fertigation systems in W. somnifera cultivation has been described to promote root development and increase withanolide concentration through maintaining optimal nutrient supply during the entire growth period (Kumar et al., 2022a).

The commercial importance of these approaches is increasingly reflected in the expanding global trade of medicinal and aromatic plants (MAPs). The essential oils sector alone had a trade of approximately USD 9.5 billion in 2021, and it was predicted to reach

Table 1: Applications of GIS techniques in medicinal and aromatic plant (MAP) cultivation.

MAP Species	GIS Application	Precision Benefit	Reference
Rosa damascena	Slope and elevation mapping using DEM	Improved site selection for oil-rich flowers	Kumar et al., 2022
Mentha arvensis	Nitrogen application zone delineation	Enhanced menthol content and reduced N loss	Saxena et al., 2021; Gulati et al., 2025
Withania somnifera	Climate suitability modeling	Identification of new suitable growing regions	Sharma et al., 2023
Acorus calamus	Soil drainage and fertility mapping	Optimized planting site selection	Pandey et al., 2023
Ocimum basilicum	Geo-referenced pest outbreak tracking	Targeted pesticide application and reduced residue	Bakhshi et al., 2022
Aloe vera	GPS-assisted harvest scheduling	Quality maintenance and labor efficiency	Rana et al., 2022

Table 2: Applications of variable rate technology (VRT) in medicinal and aromatic plant (MAP) cultivation.

Crop	VRT Application	Observed Outcome	Study/Source
Mentha arvensis	Variable nitrogen fertilizer dosing	Increased menthol yield, reduced N leaching	Singh and Bansal, 2019
Ocimum basilicum	VRT-based irrigation scheduling	Improved linalool concentration, enhanced WUE	Rahimi and Gholami, 2020
Cymbopogon flexuosus	Precision pesticide application	Targeted pest control, lower residue in essential oils	Alam et al., 2021
Withania somnifera	Variable compost application	Higher withanolide content and biomass uniformity	Kapoor and Desai, 2022
Aloe vera	VRT micronutrient foliar spraying	Increased aloin concentration, optimized nutrient use	Shakya et al., 2018
Lavandula angustifolia	Targeted drip fertigation	Increased oil yield and improved plant health	Mahmoodi and Tabrizi, 2021

around 15 billion USD in 2027, primarily powered by growing consumer demand for best, residue-free, and sustainably produced products (Markets and Markets, 2022). Countries like India, China, and Egypt are dominating the medicinal plant products export sector, and are increasingly recognizing the significance of integrating digital and precision cultivation methods to meet international standards and quality.

Moreover, climate change is the main threat to medicinal plant cultivation, but precision agriculture provides a way to climate resilience. Tools like predictive analytics and AI-based decision support systems can help in detecting disease risks, predicting irrigation needs, and recommending adaptive responses based on localized Wheater forecast, necessary for medicinal plants as their cultivation in made in ecologically sensitive zones or rain-fed areas, where crop failure or suboptimal growth can result in notable economic losses (Pandey et al., 2023).

Application of precision agriculture, in the cultivation of medicinal plants, aligns with dual objectives of enhancing phytochemical yield and ensuring sustainability. By enabling real-time, site-specific management, PA not only enhances the agronomic performance of MAP cultivation but also helps to meet global demand for optimized, high-quality plants and products. As the sector moves toward greater commercialization and regulatory oversight, precision agriculture will be contributory in updating

Table 3: Applications of AI and decision support systems in medicinal and aromatic plant (MAP) cultivation.

MAP Species	AI/DSS Technology Used	Practical Outcome	Reference
Ocimum basilicum	SVM model for oil yield prediction	Site-specific fertigation scheduling	Bhatnagar et al., 2022
Withania somnifera	CNN for disease classification	Rapid identification of leaf spot and wilt symptoms	Alam et al., 2021
Mentha spicata	Random Forest model using weather and soil data	Predictive analysis of essential oil variability	Devi et al., 2022
Cymbopogon flexuosus	DSS with IoT + AI models	Optimized irrigation and harvest decisions	Raut and Sharma, 2021; Pandit et al., 2026
Lavandula angustifolia	AI-assisted weed recognition system	Reduced manual labor and precise herbicide application	Iqbal et al., 2023
Aloe vera	Blockchain + AI-enabled traceability module	Quality control and certification for export markets	Joseph and Chatterjee, 2023

Table 4: Applications of automation and robotics in the cultivation of medicinal and aromatic plants (MAP).

MAP Species	Automation/Robotic system	Function and outcome	Reference
Ocimum sanctum	Automated transplanter	Enhanced seedling survival and uniform crop establishment	Menon et al., 2020
Aloe vera	Vision-guided robotic harvester	Damage-free leaf collection with improved aloin retention	Gupta and Rao, 2021
Lavandula angustifolia	Autonomous harvesting platform	Precision harvesting at ideal bloom stage, better oil quality	Baral et al., 2022
Withania somnifera	Robotic sprayer with disease recognition camera	Fungicide delivery to disease spots, minimal residue formation	Khanna and Bose, 2020
Cymbopogon citratus	Soil sensor robot with EC and moisture probes	Early detection of nutrient stress and variable input mapping	Prasad et al., 2023
Mentha arvensis	Robotic inter-row cultivator	Efficient weed removal, reduced herbicide use	Rizvi et al., 2022
Curcuma longa	Automated rhizome digger	Improved harvesting efficiency, reduced damage to rhizomes	Singh et al., 2020
Rosmarinus officinalis	Autonomous greenhouse climate controller	Optimized humidity and ventilation for higher essential oil biosynthesis	Rahman and Pathak, 2021
Eclipta alba	Robotic seeder with GPS guidance	Uniform planting depth and spacing, synchronized flowering	Verma et al., 2023

MAP production systems and safeguarding their long-term practicality.

Technologies driving precision cultivation of MAPs

Remote sensing and UAVs

Remote sensing is one of the most innovative approaches in precision agriculture, offering non-destructive, real-time observation of crop and field parameters across varying spatial and temporal scales. Medicinal and aromatic plants (MAPs), which are highly sensitive to slight change climatic conditions, nutrient status, and abiotic stress, remote sensing, particularly via unmanned aerial vehicles (UAVs), provides an efficient method to standardize cultivation approaches and improve the yield and quality of medicinal plants. UAVs, commonly known as drones, are equipped with multispectral, hyperspectral, thermal, and high-resolution RGB sensors. These cargos enable gathering of information about main vegetation indices such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Photochemical Reflectance Index (PRI), which are skimpy of plant vigor and photosynthetic efficiency (Rana et al., 2022). For medicinal plants, these indices are mainly significant due to fluctuations in vegetative vigor that directly influence the synthesis and accumulation of phytochemicals (Bahuguna et al., 2022; Yousefzadeh et al., 2022).

Research indicates that multispectral imaging using unmanned aerial vehicles (UAVs) can efficiently recognize initial indicators of drought in Rosa damascena, allowing timely application of irrigation to avoid decline in flower quality and essential recovery (Bakhshi et al., 2022). In M. arvensis, through drone-assisted spectral analysis, chlorophyll content was measured. Similarly, quantification of menthol was assessed under various nitrogen levels (Saxena et al., 2021; Gulati et al., 2025). Similarly, thermal imaging techniques have been effectively used for the detection of heat stress in O. basilicum, offering insightful information for the management of canopy temperature and the detection of plant stress factors. This information is also helpful for scheduling irrigation, which leads to a stable essential oil profile and overall quality of medicinal plants (Kour and Rana, 2020; Pineda et al., 2020).

IoT sensors and smart monitoring

The backbone of precision agriculture is the Internet of Things (IoT), which enables unified real-time monitoring of crucial stages of crop and environmental attributes (Moghadam et al., 2026). In the case of medicinal plants, where consistency in quality and quantity of phytochemicals is mainly dependent on the micro-environment, IoT-based sensor networks offer immense potential to optimize input use. IoT comprises field-deployed sensor nodes, wireless networks, cloud-based data processing systems, and user-oriented interfaces that work collectively to capture and transmit real-time data for informed decision-making. These sensors have the ability to record a diverse set of agro-environmental data, including soil moisture, temperature, electrical conductivity, humidity, due drop intensity, light intensity, and canopy temperature. The collected information is transmitted via wireless networks like LoRa, Wi-Fi, or ZigBee to a central cloud, where advanced analytics transform raw information into actionable recommendations for crop management (Jawad et al., 2022; Mohammed and Munir, 2025).

Water use efficacy is based on efficient irrigation

scheduling at the critical growth stage of medicinal plant production, as soil moisture directly influences essential oil recovery and overall plant secondary metabolites. In a case study, observations were recorded that a reduction in soil moisture directly reduced eugenol content in O. sanctum; however, excessive irrigation in M. arvensis resulted in low oil contents and poor quality (Saxena et al., 2021). The application of soil moisture sensors integrated with automated irrigation systems provides accurate regulation of water supply (Meriç, 2025).

Another key area for the application of IoT is nutrient regulation in medicinal and aromatic plant production. Actually, monitoring of essential macronutrients, particularly NPK, can be attained by soil nutrient probes and ion-selective electrode sensors positioned in the rhizosphere. A precise fertilizer application schedule is obtained through the integration of IoT-based systems with local weather forecasts and crop growth simulation models, which improve nutrient uptake efficiency and overall plant vigor. Sensor-controlled fertigation in W. somnifera has shown significant improvement in withanolide concentration (Kumar et al., 2022). Plant physiological status within the canopy and even at the leaf level can be detected via advanced sensing tools. Continuous monitoring of parameters such as transpiration rate, leaf temperature, and chlorophyll fluorescence provides initial detection of water and nutrient stress before visual symptoms occur, as done in Aloe vera (Rana et al., 2022). Furthermore, precise monitoring tools play a significant role in pest and disease management of medicinal plant production. It is very important in the case of medicinal plans, where maintaining a residue-free product is essential for therapeutic applications (Kour and Rana, 2020; Li and Wang, 2024).

Geographic information systems (GIS)

Precision agriculture is mainly based on GIS, as it provides the collection, storage, analysis, and visualization of spatial and temporal data from agricultural fields and is helpful in making accurate decisions (Mathenge et al., 2022). However, when GIS tools are employed in medicinal plants cultivation, helpful in detecting variation in soil fertility and crop conditions, thus offering informed decision making for specific site management. Generally, medicinal plants are being cultivated on marginal lands while fertile lands are under the cultivation of cash crops. Comprehensive information about the spatial mapping of these fields through GIS technology allows growers to define zones based on variations in soil texture, topography, and fertility (Pandey et al., 2023). In the case of R. damascena and Acorus calamus, Digital Elevation Models (DEMs) have been effectively employed to optimize planting sites to ensure better root development and essential oil recovery (Kumar et al., 2022).

Furthermore, GIS tools help integrate different source data, such as images taken from satellites, field sensor data, and UAV maps, to monitor plant growth, pest attacks, and drought. Accurate field operations could be applied through GPS data, which is helpful to manage different field operations like irrigation, fertilizer application, and harvesting at the optimum stage, which are important for maintaining phytochemical yield in medicinal plants such as M. arvensis. Nitrogen application zones with the help of GIS have resulted in stable menthol production (Saxena et al., 2021).

For the cultivation of high-value medicinal plants, GIS is also helpful for planning and assessing the environmental impact on organically produced medicinal plants. Crop history can also be tracked through spatial databases developed by GIS; furthermore, the application of various inputs and disease outbreak patterns may also be recorded, which is most important for traceability and regulatory compliance (Bakhshi et al., 2022). Models such as these incorporate variables such as temperature, rainfall, humidity, and solar radiation to predict potential yield areas and guide expansion approaches. For example, climate suitability mapping has been effectively used to assess future cultivation areas for W. somnifera and O. basilicum under climate change scenarios (Alam et al., 2021a; Sharma et al., 2023). Practical implementation of GIS in MAP cultivation, however, requires precise field data, skilled personnel, and supportive digital infrastructure. Limited access to spatial tools in rural areas and the high cost of commercial GIS software can be barriers. Nonetheless, the availability of open-source GIS platforms (e.g., QGIS) and the proliferation of mobile-based GPS applications are making geospatial technologies increasingly accessible to MAP farmers and cooperatives.

Variable rate technology (VRT)

Variable Rate Technology (VRT) enables site-specific application of agricultural inputs like fertilizers, irrigation, and crop protection agents based on real-time data and geospatial analysis. Precision-based method holds significant relevance in the cultivation of medicinal and aromatic plants (MAPs), where spatial variability in soil fertility, microclimate, and crop requirements mainly influences secondary metabolite synthesis and overall crop yield.

The quality of medicinal plant products is affected by uneven application of inputs, i.e., fertilizer and pest control chemicals. Variable Rate Technology (VRT) helps address such challenges by enabling modification of input doses to meet the actual requirement of a crop (Saleem et al., 2023). In the case of peppermint (Mentha piperita), application of nitrogen fertilizer for a specific zone or site guided by prescription maps notable increase in concentrations of menthol was recorded, and indirectly minimizes the cost of nutrients through precise application (Yadav et al., 2021). Similarly, targeted irrigation in Cymbopogon martinii using VRT systems improved geraniol yield, a key component of essential oil, by precisely matching water delivery with the crop’s peak evapotranspiration demands (Das et al., 2022). Similarly, a potassium dose was applied to the ashwagandha (W. somnifera), which showed improvement in both withanolide and root biomass (Meena and Solanki, 2020). Furthermore, pest management was made through VRT-equipped sprayer machines, targeting pest holy basil (O. sanctum), which resulted in minimizing pesticide which ensure residues are free as per standards demanded by the pharmaceutical industry (Chopra et al., 2021). Prevention of nutrient runoff and environmental sustainability can be achieved through the application of Variable Rate Technology (VRT), as medicinal plants are being cultivated in environmentally sensitive zones across the globe, which is why it is important for sustainability. VRT offers a uniform phytochemical composition, which highlights its significance in medicinal plant production (Mishra and Patra, 2023; Chen, 2025).

Apart from its confirmed advantages, the application of Variable Rate Technology (VRT) in the cultivation of medicinal and aromatic plants is still limited due to several constraints. Main factors include installation cost, limited geospatial data, and inadequate technical training for growers. Moreover, mostly VRT systems are built for large-scale farming and crop-specific, while monoculture farming, making them difficult to adapt to smallholder operations typical of MAP cultivation. Therefore, developing the cheapest and customized VRT solutions can maximize their application for revolution in the field of medicinal and aromatic plant production on a sustainable basis.

Artificial intelligence and machine learning

Artificial Intelligence (AI) and Decision Support Systems (DSS) are playing an increasingly transformative role in the global landscape of Medicinal and Aromatic Plant (MAP) production. Yield and crop management for a specific place can be assessed for growers and researchers could be possible through artificial intelligence (Maryam et al., 2025). Sustainability in yield and phytochemicals is necessary for the medicinal and aromatic plant production, within a limited production cost, which is possible through AI tools. Multisource data like spectral imagery, meteorological forecasting, and soil moisture could be analyzed through AI tools, including Deep Learning (DL), Machine Learning (ML), and fuzzy logic. For instance, in specific medicinal plants like sweet basil (O. basilicum) and spearmint (Mentha spicata), supervised ML models have been successfully employed to assess the yield of biomass and essential oil content based on integrated environmental and cultivation attributes (Singh et al., 2023).

Convolutional Neural Networks (CNNs) have shown high efficiency when used for the identification of diseases and pests in different crops, such as W. somnifera and Lavandula angustifolia, using image-based categorization. Production losses can be minimized through early identification of pathogens and abiotic stress symptoms through AI tools and timely application of agrochemicals and organic compliance, often required for export-grade MAPs (Tiwari et al., 2022). Decision Support Systems (DSS) and AI algorithms work together as they are integrated with agronomic databases, remote sensing, and climate predictions. For example, decision support systems tools particularly developed for Cymbopogon flexuosus plantations in India use soil moisture sensors, evapotranspiration, precision pesticidal delivery system, and weather forecasts to help determine when and how much irrigation water is needed to apply, which directly affects geraniol content in essential oils (Alam et al., 2021b; Panda et al., 2023).

Monitoring, traceability, and quality are also improved through AI. Integration of smart labels and blockchain technologies with AI permits tracking of product origin, inputs, and batch characteristics. This guarantees authenticity in MAP supply chains, particularly for high-value crops like Aloe vera and Boswellia serrata, which are liable to contamination. Despite their potential, AI adoption in MAP farming is limited by the unavailability of crop specific database, a lack of digital knowledge among growers, and cost barriers. Collaborations between research organizations, government programs, and tech companies are important to develop open-access tools and low-cost decision platforms customized for medicinal and aromatic plants growers.

Automation and robotics

Integration of automation and robotics into precision agriculture has opened new horizons for improving efficiency, precision, and labor optimization in the cultivation of medicinal and aromatic plants. These technologies enable site-specific operations such as transplanting, harvesting, spraying, and monitoring, especially critical for high-value crops that demand minimal contamination, uniformity, and the best harvest timing. Medicinal plants require delicate handling to preserve the integrity of bioactive compounds. For example, robotic harvesters equipped with soft grippers and vision systems have been developed to selectively pick Aloe vera leaves based on their maturity index, ensuring maximum aloin content while avoiding damage to younger leaves (Gupta and Rao, 2021; Mitra et al., 2022). Harvesting of L. angustifolia flowers through automated harvesting platforms has significantly increased harvesting speed and oil recovery by avoiding over-mature or underdeveloped flowers.

Automated mechanical transplanting systems are getting popular for medicinal and aromatic plants nurseries, especially for crops such as O. sanctum, Mentha spp., and Eclipta alba. As these systems ensure uniform plant distance, minimize transplanting shock, and help establish early crop uniformity, which is important for synchronized flowering and sustainable essential oil profiles (Nayyar et al., 2021). Similarly, robotic sprayers are making a notable impact on the efficacy of the spraying process in disease and pest management. These tools can autonomously navigate in the fields using GPS and LiDAR technologies and are capable of efficient spraying for pest and disease control in medicinal fields. Fungal infection in W. somnifera fields can damage the root biomass production up to a notable level. Automated sprayers equipped with multispectral cameras have been used to detect disease and spray fungicides with high precision (Choudhary and Das, 2023).

Robotic platforms have significantly advanced the monitoring of crops and the analysis of agricultural soils. Ground-based rovers, integrated with multi-sensor arrays, are used to collect soil samples, monitor temperature, moisture, and electrical conductivity, and identify nutrient-deficient patches in real-time. These robots have been especially useful in evaluating micro-site variability in Cymbopogon and Vetiveria plantations established on degraded lands. Although the initial investment for robotic solutions is high, their long-term benefits, such as consistent quality, reduced labor dependency, and efficient resource use, are driving adoption, particularly among commercial MAP growers and cooperatives. Affordable, small-scale automation solutions are also being developed for smallholder applications in developing countries.

Case Studies and Applications

Practical application of technologies in precision agriculture for the cultivation of medicinal plants has developed in recent years; various case studies have been conducted in this regard for the assessment of its significance. These case studies showcase how different tools aligned with sensors, remote monitoring, variable rate inputs, and automation improve yield and quality with minimal cost of production.

India: Lemongrass and holy basil in the indo-gangetic plains

Extensive cultivation of C. flexuosus (lemongrass) and O. sanctum (holy basil) plantations have made in India’s Indo-Gangetic plains. For irrigation IoT systems with combination of soil moisture sensors has been adopted, resulting in up to 30% water saving through this integration without compromising oil yield and phytochemical profile. Furthermore, NDVI imaging technology was used to optimize nitrogen use, which resulted in a 15–20% increase in oil content per hectare (Das et al., 2022). These technologies were adopted through cooperation of the National Medicinal Plants Board (NMPB), aiming to make MAP farming profitable and sustainable.

Morocco: Saffron cultivation in arid highlands

Remote sensing and UAVs were used in the Taliouine region of Morocco for monitoring to assess plant vigor and drought in terraced fields of Crocus sativus (saffron). Satellite imagery, paired with ground validation, facilitate to identify micro-zones requiring additional irrigation. Flowering is associated with precision nutrient mapping, which improves flower intensity, but with the deployment of remote sensing, phosphorus application was reduced up to 28% without compromising crocin concentration (El Fadili et al., 2021).

China: Panax notoginseng under greenhouse automation

Panax notoginseng is an important medicinal plant, cultivated in controlled conditions in Yunnan Province, China. AI helps in decision-making based on soil moisture, humidity, temperature, and disease-identifying sensors were integrated. Real-time alerts helped growers in operating foggers, fungicide sprays, and ventilation management as needed. Chemical inputs were reduced up to 42% and improved ginsenoside consistency in all batches. The initiative was part of a provincial smart farming trial by Kunming Institute of Botany (Zhang et al., 2023).

Italy: Lavender and rosemary fields in mediterranean agroecosystems

Autonomous harvesting of L. angustifolia and R. officinalis fields in Tuscany was adopted. Harvesting time was optimized with the help of these autonomous tools by detecting peak bloom and oil content windows without wastage of time. Adoption of variable rate fertigation also enhanced essential oil density while reducing nitrogen level up to 18%. Regional cooperatives supported training in precision agriculture technologies to reduce manual labor dependency and increase product traceability (Ricci et al., 2020).

Brazil: Aloe vera and native MAPs in the semi-arid caatinga

Solar driven smart irrigation system was adopted for the cultivation of Aloe vera by a small landholder of sami-arid Caatinga region. These systems, connected to mobile DSS platforms, monitored water requirement based on solar radiation and crop phenology. Improved irrigation efficiency improved aloin content and minimized root rot occurrences. Trials on native MAPs like Lippia sidoides also established improved thymol yield through sensor-guided fertilization (Silva et al., 2021).

Challenges and limitations

Precision Agriculture (PA) promises to improve the efficiency and sustainability of medicinal and aromatic plant (MAP) production. However, its extensive adoption is hindered by several interrelated technical, economic, and infrastructural challenges that are especially pronounced in medicinal and aromatic plants systems compared to major cereal crops (Ahmadi et al., 2025).

High initial investment and equipment cost

One of the principal limitations is the significant upfront cost associated with PA technologies. These include GPS-guided machinery, variable rate applicators, UAVs, multispectral cameras, and soil-sensing equipment. Many MAPs, such as Ocimum, Vetiveria, and Lemongrass, are cultivated in small-scale systems where return on investment is marginal. In a recent study across herbal cooperatives in Nepal and northern India, 78% of growers reported that PA equipment was unaffordable without external subsidies (Shrestha et al., 2022; Manickam et al., 2023). Furthermore, PA manufacturers often tailor equipment to large-scale monocultures, leaving MAP growers without appropriate, cost-effective models.

Lack of crop-specific data and models

The major limiting factor is the unavailability of crop-specific spectral databases, nutrient thresholds, and yield prediction models for most MAPs. While a database exists for cereals and vegetables, few calibration datasets are available for Ashwagandha, Tulsi, Kalmegh, or Sarpagandha. This limits the precision of machine learning algorithms, remote sensing indices, and fertigation models when applied to MAPs (Olatunji et al., 2023; Figueroa et al., 2022). Furthermore, even in greenhouse MAP systems, uniformity in phenological modeling is missing, complicating automation protocols.

Limited digital and agronomic literacy

Digital literacy among growers remains a major constraint. Many smallholder MAP farmers are unfamiliar with data interpretation, app-based watching, or cloud-connected platforms. In a study of M. arvensis and A. vera cultivators in eastern Uttar Pradesh, only 18% of respondents had used mobile-based DSS platforms and 62% relied solely on visual cues for crop decisions (Tariq et al., 2022; Rathod et al., 2021). Extension agents often lack updated training in PA tools, leading to a mismatch between available technology and on-ground support.

Infrastructure and connectivity gaps

Use of sensor-driven systems and cloud-connected tools requires stable internet, GPS signals, and regular power supply conditions that are often missing in remote MAP-growing regions. In Ethiopia, for example, high-value aromatic species such as Eucalyptus citriodora and Thymus schimperi are being cultivated in rural areas where connectivity issues restrict the use of automated irrigation and remote field sensing (Tadesse et al., 2022).

Weak integration with quality certification systems

Precision agriculture systems can generate valuable traceability and quality assurance data. However, many certification bodies still depend on manual documentation. Absence of standardization in Precision agriculture outputs means that current data often cannot be directly applied for certification or export documentation, limiting growers’ motivation to adopt such systems (Leung et al., 2023; Jamwal et al., 2023).

Benefits of smart agriculture for MAPs

Application of smart agriculture approaches in the production of medicinal and aromatic plants (MAPs) provides multifaceted advantages that extend beyond productivity improvement. These technologies, including precision irrigation, automated harvesting, disease identification, remote sensing, automated cultivation systems, and artificial intelligence-driven decision tools, permit growers to address unique physiological, environmental, and economic aspects associated with MAP farming.

Improved yield and biomass uniformity

Medicinal and aromatic plant production often targets phytochemical yield rather than biomass alone. Precision agriculture techniques help to optimize input use based on crop requirements, which leads to uniform growth and biomass on a sustainable basis. For example, variable-rate nitrogen application in M. arvensis enhanced both biomass and menthol content by associating nutrient availability with the plant’s developmental stage (Kumar et al., 2022b). Similarly, remote sensing technology has assisted early detection of growth variability in O. basilicum, improving field management approaches and leading to a 15% increase in dry matter (Silva et al., 2021; Zhong et al., 2025).

Enhanced phytochemical quality

Concentration of secondary metabolites in MAPs such as essential oils, alkaloids, and flavonoids is extremely sensitive to environmental conditions. Accurate control of microclimatic conditions through greenhouse automation and real-time observing of soil pH, temperature, and humidity leads to improved phytochemical profiles. In L. angustifolia, smart irrigation systems maintained optimal stress levels that improved linalool and linalyl acetate content, both main indicators of commercial oil quality (Tiwari and Joshi, 2023).

Efficient resource utilization

Wastage of resources can be minimized through smart agriculture by enabling need-based applications without compromising yield and quality. In Cymbopogon citratus, water use was minimized up to 40% by controlling drip irrigation systems via soil moisture sensors, with no loss in oil yield. Similarly, site-specific herbicide application reduced chemical use by up to 60% in Rosmarinus officinalis plantations, where autonomous sprayers targeted only weed-affected zones (Pereira et al., 2022).

Disease and pest early warning

Sensor networks and AI-based image analysis facilitate early detection of biotic stress, allowing growers to implement targeted and timely interventions. In W. somnifera, spectral imaging systems identified foliar blight at its early stages, leading to a 50% reduction in yield loss compared to the conventional scouting (Raut and Mishra, 2021). The integration of such tools reduces unnecessary pesticide applications and supports organic certification.

Improved labor efficiency and cost reduction

Intensive labor is required in medicinal and aromatic plant production, especially for transplanting, weeding, and harvesting. Automation and robotics minimize the dependency on labor, which is necessary in regions where labor availability is limited. Trials with semi-autonomous harvesters in Aloe vera fields showed a 45% reduction in harvesting time and labor cost per hectare (Benitez et al., 2023).

Data-driven traceability and market access

With increasing international scrutiny over herbal product authenticity and safety, traceability is becoming a competitive advantage. Smart agriculture allows the generation of verifiable data on cultivation practices, input history, and harvest records. Blockchain-integrated traceability platforms, now emerging in high-value MAPs such as Panax ginseng and Boswellia serrata, support compliance with WHO-GACP and organic standards, facilitating access to premium export markets (Chakrabarti et al., 2022; Zamani et al., 2025).

Future perspectives

The intersection of medicinal and aromatic plant (MAP) cultivation with smart agriculture technologies is entering a transformative phase. Although adoption remains uneven, emerging innovations in sensors, artificial intelligence (AI), data integration, and automation are expected to substantially improve yield quality, sustainability, and traceability in MAP production systems. Going forward, addressing infrastructural, socio-technical, and policy-level gaps will be pivotal in realizing the full potential of precision agriculture in this sector.

Development of MAP-specific digital models

There is a clear need for crop-specific modeling tools tailored to the unique physiology and secondary metabolite pathways of MAPs. Investment in AI and machine learning algorithms for forecasting plant growth, essential oil profiles, and disease susceptibility will be essential. Establishing open-access databases containing spectral signatures, growth patterns, and soil interactions for key MAP species (e.g., O. basilicum, Curcuma longa, Panax notoginseng) will enable more accurate and site-specific decision-support systems (Rana et al., 2022; Sharma et al., 2023).

Scalable and affordable precision tools for smallholders

Future efforts should focus on the design and deployment of low-cost, solar-powered, and modular PA systems that cater to small and marginal MAP growers. This includes simplified IoT kits, SMS-based advisory systems, and smartphone-integrated sensing platforms. Pilot projects in India and Ethiopia have demonstrated the viability of such scaled-down systems in increasing the profitability of MAP production (Binyam et al., 2022; Patel and Jain, 2023).

Blockchain and certification integration

Integrating blockchain platforms with sensor-based traceability data can redefine quality assurance mechanisms in the MAP value chain (Joseph and Chatterjee, 2023). These systems should be aligned with the WHO-GACP, USDA Organic, and EU Organic protocols. Transparent digital records from sowing to post-harvest handling can enable seamless audits, build consumer trust, and facilitate access to high-value international markets (Lopes et al., 2022; Gurusamy et al., 2025).

Multidisciplinary training and capacity Building

Capacity building for medicinal and aromatic plant growers is a key step toward enhancing their ability to use digital tools effectively. This is particularly important in rural and tribal regions where most high-value MAP species are cultivated. To achieve this, agricultural universities and extension services should design practical training programs and demonstration sessions focused on modern cultivation techniques, the operation of digital sensors, and the use of crop-monitoring technologies (Gupta and Awasthi, 2022; Priya et al., 2025).

Public-private partnerships and policy support

Supportive national and international policies are needed to encourage innovation and make it easier for technology developers to enter the precision agriculture (PA) market for medicinal and aromatic plants (MAPs). Collaboration between the public and private sectors can help fund pilot projects, promote research, and expand successful approaches. Practical policy measures such as tax relief, targeted programs for precision MAP farming, and subsidies for digital inputs can further promote the widespread use of these technologies in agricultural production sectors (Hasan et al., 2023; Khaspuria et al., 2024).

Conclusions and Recommendations

Application of precision agriculture is limited in medicinal and aromatic plant production, although its significance is well recognized. Key obstacles include higher initial costs, limited computer literacy, poor infrastructure, and no attractive schemes for growers, particularly small landholders. Apart from these obstacles, the main issue is that these AI tools are site- and species-specific, and cannot be customized by growers according to their needs and crops. Recently, however, several practical solutions have emerged, including affordable sensors, mobile-based advisory tools, and blockchain systems that enable product traceability. These innovations can make precision farming more accessible and cost-effective, especially for small and medium producers seeking to improve efficiency and profitability. Progress will depend on close collaboration between researchers, policymakers, and farmers to address the existing gaps and create supportive frameworks. With the right institutional and technical backing, precision agriculture could substantially raise yields, improve quality, and help MAP growers to meet the international standards and certification requirements.

Acknowledgments

The authors are grateful to the Department of Horticulture Science, Agricultural College, the University of Al-Qadisiyah, Iraq for guiding the research process.

Novelty Statement

This is the first study by using the smart agriculture in improving medicinal and aromatic plant production.

Author’s Contribution

Ali Sabah Alhasan: Carried out the literature collection and drafted the manuscript.

Hayder Abbas Drebee, Osama Majeed Hilal, Alaa Taima and Dhafer Alhajim: Participated and carried out the table and figure preparation.

Dalal Tareq Al-Ameri: Conceived the idea and critical review of the manuscript.

Ali Fadaam Almehemdi, Raad Farhan Shahad, Hayyawi WA Al-Juthery and Raid Shaalan Jarallah: Participated and improved the manuscript.

All authors read and approved the final manuscript.

Generative AI and AI-assisted technology statement

The authors declare that no generative AI and AI assisted technology was used in the creation of this manuscript.

Conflict of interest

The authors have declared no conflict of interest.

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