Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy.

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation.

Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation.

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.

The role of IoT in every aspect of human life is capacious. IoT being immensely popular technology, offers every possible solution to modernize life. Due to the omnipresence of internet, IoT offers substantial growth in almost all industrial domains. IoT is expected to make a remarkable difference in the revenue generated by various sectors, by combining it with other latest technologies. But this may also lead to unforeseen risks and challenges. A systematic review has been done, by conducting a survey of the detailed impact of IoT on various industrial domains. The research also explores a novel approach to integrate various technologies like fog and cloud with IoT. Various challenges encountered in the growth of IoT have also been analyzed, including security challenges and different methods to elucidate the issues have also been studied. Moreover, a standard generalized architecture of IoT technology has been presented which encompasses all the application domains. Lastly, different kinds of open issues and challenges have been discussed so that the researchers can work efficiently in future in various domains of IoT.

BACKGROUND Patient medical records remain frag-mented across hospitals, laboratories, and clinics, preventing clinicians from accessing complete longitudinal health information. Emergency blood and organ allocation further suffers from time delays that significantly increase mortality risk. Conclusions: OBJECTIVE This study proposes and evaluates a national-scale centralized health record platform integrating standardized FHIR-based data aggregation, longitudinal artificial intelligence analytics, and emergency blood and organ donor discovery networks. METHODS All diagnostic laboratories mandatorily upload test results using HL7 FHIR standards to cre-ate unified patient records. Machine learning models including Random Forest classifiers, ARIMA time-series forecasting, geospatial matching, LSTM net-works, and explainable AI techniques were applied for donor eligibility, blood shortage prediction, and longitudinal disease tracking. Large-scale synthetic datasets were generated to simulate national deploy-ment scenarios. RESULTS The Random Forest model achieved 100% recall for donor eligibility detection. ARIMA forecasting predicted blood shortages with 89% accuracy, and geospatial matching identified compatible donors within a 5 km radius. Simulation of 2,000 emergency blood requests demonstrated a 76% re-duction in delivery time (58 to 14 minutes) and fulfillment improvement from 82% to 95%. Availability of rare blood types increased by 27–33%. CONCLUSIONS Centralized FHIR-based health data combined with longitudinal AI analytics and real-time donor discovery networks can substantially improve emergency response, disease management, and healthcare equity at national scale. Keywords: FHIR; electronic health records; med-ical informatics; machine learning; longitudinal disease analysis; emergency blood donation; interoper-ability