Abstract: Heterogeneous forests often contain multiple tree species, with complex hierarchical structure in their canopy layers．Different vegetation level exhibit varying reflections, scatterings, and absorptions of light, resulting in complex spectral features．Feature wavelengths selected based on a single imaging slice affect disease and pest identification accuracy．Therefore, a spectral identification technology for heterogeneous forest canopies diseases and pests based on imaging slice continuity was proposed．Continuous projection of imaging slices for heterogeneous forest canopies diseases and pests was performed．Based on absolute values of projection vectors across continuous imaging slices set, wavelengths were iteratively updated to select feature wavelengths covering continuous imaging slices．Using top N feature wavelengths with the largest projection vector, extended ratio spectral indexes were calculated and mapped to Lambe angle field of heterogeneous forest canopies disease and pest images．Based on mapping results, GAFS was used to generate spectral distribution information of heterogeneous forest canopies diseases and pests through inverse inference, completing spectral recognition．Test results indicated that design method performed well in recognition accuracy, and, with Kappa value remaining stable above 0.6 after 15 iterations, indicating recognition stability．