Water Imaging of Living Corn Leaves Based on Near-Infrared Hysperspectral Imaging

Non-destructive detection of plant leaves water content is of significance to plant physiological, biochemical research, irrigation management and drought monitoring. In this paper, Gaia Sorter Near-Infrared Spectrometer (900 similar to 1 700 nm) was used to detect the water content of 60 fresh corn leaves in different growth stages using PLS and SMLR models. The results demonstrated that the R-2/SEP of validation set were 0.975/1. 18, 0.980/1.02, all achieving better predictive results, which could bring out the determination of a single corn leaf average water content. The results of the SMLR model established with the preferred characteristic wavelength (1406 nm, 1692 nm) indicated that the use of high-throughput near-infrared camera combining filter method achieved the feasibility of corn leaves canopy or high-altitude remote sensing measurement. Simultaneously, the imaging analysis of the water content in different regions of the leaves was carried out, and the results revealed that the correlation coefficients between the measured mean values and the predictive mean values of the mesophyll and the main vein of the six leaves were 0.85, and the predictive results were in accordance with the actual situation.