Intelligent dual-modal hyperspectral imaging system with deep learning for in-field soil fauna identification

Accurate and efficient identification of living soil fauna is crucial for ecological biodiversity assessment and sustainable agriculture practices, yet traditional methods are labor-intensive, time-consuming, and often ineffective in complex field environments. This study pioneeres a novel dual-modal hyperspectral soil fauna identification (HSFI) system, which ingeniously Integrates both reflectance and fluorescence hyperspectral imaging with a custom-designed deep learning model, HSFI-Net, for robust semantic segmentation. This synergistic approach effectively overcomes the challenges of rapidly and accurately identifying soil fauna, even those partially concealed with heterogeneous soil backgrounds. Using the HSFI system, we established a comprehensive dual-modal spectral database for diverse soil macrofauna, including earthworms, centipedes, scorpions, pillworms, millipedes, crickets, beetles, ants and so on. Extensive experimental evaluations demonstrated the system’s exceptional robustness and high precision, achieving average IoU of 0.734 across various exposure levels and various species under challenging soil conditions. Furthermore, in-field experiments successfully validated the system’s capability for in-situ identification and analysis of soil fauna’s horizontal and vertical distribution. This innovative HSFI system offers an automated, intelligent tool for monitoring soil biodiversity, which is vital for precision agricultural management, environmental conservation, and understanding the intricate dynamics of soil ecosystems.