ArticleScientific reports2024
Internet of things (IoT) based saffron cultivation system in greenhouse.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Advancing climate adaptation in saffron through CRISPR-based modulation of stress tolerance and photoperiodic flowering control.GM crops & food · 2026Review
- Validating the invisible: A mixed-methods approach to establishing reliable WEF nexus indicators for sustainable organic saffron production.Scientific reports · 2026Article
- Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs.
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