Tomato growth monitoring under different n treatments based on digital image analysis

Tomato canopy and background pixel was identified by image processing software with CCD digital camera as a sensor. Percent ground cover of vegetation (PGCV) and tomato biological parameters were adopted for regression analysis. Empirical statistical showed that there was a significant correlation between these two parameters (r=0.96). The results of regression analysis showed that the optical parameters (Dark Green Color Index, DGCI) significantly linear correlated to tomato leaves’ chlorophyll concentration (r=0.74). The results also indicated that image analysis is a promising method for monitoring growth rate of tomato at the prophase. The PGCV is a reliable indicator which can be used to evaluate both the biomass and the leaf area index (LAI) of tomato plant throughout the whole growth seasons. DGCI can be used to estimate the chlorophyll concentration in tomato’s leaves. Therefore, color analysis provided a potential way for monitoring growth of tomato rapidly and accurately.