Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m²) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO₂ content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

This study presents a comprehensive optimization of algal oil extraction and transesterification for sustainable biodiesel production. Freshwater Spirogyra algae underwent Soxhlet extraction using n-hexane. response surface methodology (RSM), principal component analysis (PCA), and multivariate regression analysis (MRA) were employed to investigate the effects of biomass–solvent ratio (BSR), algae particle size (APS), and extraction-contact time (E-CT) on algal oil yield (AOY). The extracted oil was then converted to biodiesel via transesterification, and the impacts of the methanol–oil ratio (MOR) and transesterification-contact time (T-CT) on biodiesel conversion efficiency (BCE) were analyzed. Results demonstrate that optimal BSR, APS, and E-CT for maximal AOY are 1:7, 400 µm, and 3–4 h, respectively. For transesterification, a MOR of 12:1 and a T-CT of 4 h yielded the highest BCE. Predictive models exhibited exceptional accuracy, with R² values of 0.916 and 0.950 for AOY and BCE, respectively. The produced biodiesel complied with ASTM D6751 and EN 14214, showcasing its potential for renewable energy applications.