Ground-Based Digital Imaging as a Tool to Assess Soybean Growth and Yield

Leaf and canopy characteristics influence light interception and photosynthesis, which ultimately contribute to total biomass production and crop yield. Inexpensive hand-held digital cameras may provide an alternative to time-consuming destructive sampling methods used to assess these traits. The objective of this study was to test the use of common digital cameras as a potential phenotyping tool to predict yield and other crop traits in two soybean (Glycine max L. Merr.) cultivars subjected to differences in soil moisture availability. Weekly digital images were acquired from each plot and used to estimate canopy cover, total aboveground biomass, leaf biomass, photosynthesis, and grain yield. The method proved effective in identifying the dynamics of canopy cover and changes in biomass and grain yield as influenced by soil water availability. Further, it was possible to differentiate between cultivars and accurately determine the timing of maximum crop biomass and canopy cover. The image analysis also indicated the onset of leaf senescence which led to a decrease in total biomass around 90 to 105 d after planting. Principal components analysis showed that canopy coverage extracted from images collected spanning a period from beginning pod to seed filling was positively correlated with grain yield. Models developed using multiple regression analysis exhibited strong relationships between the ground cover data obtained using image analysis and grain yield (R-2 = 0.74), crop growth rate (R-2 = 0.69), and photosynthesis (R-2 = 0.80). Based on these results, digital imaging has potential as a tool to rapidly assess soybean canopy development and estimate biomass accumulation and grain yield.