Design of an IOT-based saffron crop irrigation system

Abstract

Saffron is a highly prized and globally demanded spice.Still, the rate of production is unable to keep up with the exponentially rising demand due to a variety of reasons. One of the major problems faced in increasing saffron growth is the lack of understanding of the best circumstances. Factors like Temperature, humidity, and light intensity play a vital role in the growth and development of plants, and continuous monitoring and control of these factors are necessary to ensure optimal growth. To address this issue this paper introduces an integrated automated system designed to empower farmers in optimizing production while simultaneously facilitating data collection for research purposes, aimed at enhancing both the quantity and quality of agricultural yields using the utilization of sensors and a live data streaming application. It allows for efficient management of saffron's growing operations and enables more accurate adjustments. The sensors can be remotely located in the saffron growing areas and connected to the app via wireless connections, allowing farmers to monitor these factors and suggest changes continuously. Using this system, farmers can ensure that the conditions for saffron plant growth are optimal at all times, leading to increased production. The developed app utilizes technologies such as ReactJS and Google Firebase, and also allows for real-time data streaming, enabling farmers to view the current conditions at any time and make adjustments accordingly. Additionally, a user-friendly mobile application is developed to enable farmers to easily oversee their cultivation conditions. This paper bridges the gap between existing research by integrating the factor of luminous intensity and its effect on Saffron during it’s budding and growth stage, an area which is severely under researched and finds almost negligible mainstream integration in similar prototype models. Furthermore, the paper underscores the potential for future machine learning models by highlighting the provision for follow-up studies using the data collected through the prototype, thus fostering ongoing advancements in agricultural research and technology.