Heavy metal (HM) pollution has become a major environmental concern due to increased anthropogenic activities. The persistence and toxicity of HMs pose significant risks to ecosystems, biodiversity, and human health. This review highlights the pressing issue of HM contamination, its impact on ecosystems, and the potential risks of bio-magnification. Addressing these issues requires sustainable and cost-effective solutions. Among various remediation strategies, phytoremediation stands out as a promising green technology for mitigating environmental damage by using plants to extract or detoxify contaminants. A key challenge in phytoremediation, however, is the management of large volumes of contaminated biomass. This study explores the integration of phytoremediation with biofuel production, which not only addresses biomass management but also offers a sustainable solution within the framework of the circular economy. The dual role of specific plant species in both phytoremediation and biofuel production is evaluated, providing reduced environmental waste, lowering remediation costs, and promoting energy security. Future advancements in plant engineering, biotechnology, and process optimization hold the potential to enhance phytoremediation efficiency and biofuel yields. Expanding research into metal-tolerant, high-biomass crops can further improve scalability and economic feasibility. The review also critically assesses challenges such as the safe handling of contaminated biomass, sustainability concerns, and existing research gaps. By merging environmental remediation with bioenergy production, this interdisciplinary approach presents a viable pathway toward sustainable development.

A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows.