Few-shot learning for classification of SEM images from green-synthesized nanoparticles of Momordica cymbalaria.

Nanotechnology offers wide range of applications owing to the unique physicochemical and biological properties of nanoparticles. Synthesis of nanoparticles from plant extracts provides a sustainable and safe alternative to the conventional chemical methods. In the current research phyto-synthesis and characterization of silver nanoparticles (AgNPs) and calcium carbonate nanoparticles (CaCO3 NPs) using fruit and root extracts of Momordica (M.) cymbalaria, is reported. The characterization was carried out using Ultraviolet (UV)–Visible spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray (EDAX) spectroscopy. EDAX confirmed CaCO3 NPs to be rich in oxygen (42–45%) and calcium (30%), while AgNPs exhibited distinct silver peaks (15–20%) along with significant carbon and oxygen content. UV–Visible spectroscopy and FTIR spectra confirmed functional groups and surface plasmon resonance peaks characteristic of the synthesized nanoparticles. The SEM analysis revealed spherical CaCO₃ nanoparticles (200–600 nm), whereas AgNPs displayed irregular aggregates (3–25 μm). To address the limited dataset of SEM images, a few-shot learning (FSL) framework was adopted for nanoparticle classification across four classes (AgNO3-root, AgNO3-fruit, CaCO3-root, CaCO3-fruit). MobileNetV2 and ResNet50 were trained for classification of SEM images. MobileNetV2 with episodic training, Mahalanobis distance, and KMeans clustering (Configuration 3) achieved 95% accuracy with a compact 8.98 MB as model size, outperforming baseline transfer learning approaches. The findings highlight the potential of automated classification of nanoparticles’ SEM images using FSL under low-data regimes.