Beyond conventional imaging: curvelet-AI convergence for non-invasive microalgal lipid quantification and biofuel optimization.

The transition toward sustainable bioenergy systems necessitates robust, scalable, and non-invasive strategies for monitoring microalgal biomass and lipid accumulation. Despite the superior productivity and carbon sequestration potential of microalgae, large-scale biofuel commercialization remains constrained by the lack of efficient real-time quantification techniques and the inherent complexity of microalgal imaging data. While existing reviews on AI-based microalgal monitoring focus primarily on model architectures, the novelty of this review lies in identifying multi-resolution image representation rather than network architecture alone as the principal determinant of analytical accuracy. Consequently, this review evaluates the image representation layer itself, assessing the Fast Discrete Curvelet Transform (FDCT) as a key preprocessing framework for deep-learning-based lipid quantification. Unlike conventional Fourier- and wavelet-based approaches, the curvelet transform provides near-optimal sparse representation of anisotropic and curved biological structures, enabling superior edge preservation and noise suppression in microscopy images. When coupled with machine learning and deep learning architectures, including Support Vector Machines, Random Forests, Convolutional Neural Networks, U-Net, and ResNet, curvelet-enhanced preprocessing significantly improves segmentation accuracy and lipid droplet detection at the pixel level. Furthermore, the review examines emerging hybrid curvelet-AI frameworks, where high accuracies (> 98%) reported in analogous imaging tasks and strong correlations with analytical standards (e.g., GC-MS) underscore their potential as reliable, non-destructive proxies for lipid quantification. Additionally, the integration of these frameworks with IoT-enabled photobioreactor systems is discussed as a pathway toward real-time, automated process optimization. However, challenges related to data scarcity, computational complexity, and imaging variability remain critical barriers. Future directions emphasize the need for standardized datasets, lightweight architectures, and multimodal data fusion to enhance scalability and industrial applicability. Overall, the convergence of curvelet-based image processing and AI offers a promising route toward scalable microalgal biofuel technologies.