Jeon, Yu-Jin Kim, Hyoung Seok Lee, Taek Sung Park, Soo Hyun Yun, Heesup Jung, Dae-Hyun 2026-02-03T03:00:19Z 2026-02-03T03:00:19Z 2026-02-02 2025-12 https://pubs.kist.re.kr/handle/201004/154102 Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture. English MDPI AG Multimodal Optical Biosensing and 3D-CNN Fusion for Phenotyping Physiological Responses of Basil Under Water Deficit Stress Article 10.3390/agronomy16010055 1 Agronomy, v.16, no.1 Agronomy 16 1 Y scie scopus 001657166600001 2-s2.0-105027857207 Agronomy Plant Sciences Agriculture Plant Sciences Article CHLOROPHYLL-A FLUORESCENCE YIELD multimodal data fusion chlorophyll fluorescence 3D convolutional neural networks (3D-CNN) plant physiological monitoring precision agriculture