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Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop's agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework's performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market. [ABSTRACT FROM AUTHOR]