In this research, winter wheat Nitrogen and water status assessment by a digital camera was studied. Field experiments were conducted during growing seasons, 2012, 2013 and 2014 in Potsdam, Germany. Different treatments of N fertilization and water regimes were applied to the plant. Aboveground biomass was sampled three times during each season, and analyzed for plant nitrogen content, fresh and dry biomasses, and water content. Color images of the plant canopy were taken in the field by a digital camera. The images were then processed and various features were derived. Multivariable models using the features and two different algorithms, Partial Least Square Regression (PLSR) and Random Forest (RF) were developed. The models were built for data of single sampling dates and combination of sampling dates of each season. In addition, spectral measurements were taken by a portable field spectroradiometer and a few vegetation indices (VIs) were calculated. Linear regression models were built between the VIs and the plant parameters. According to the results obtained, PLSR presented the strongest models for both single-date and combined-dates data. R2 (corresponding RMSE) of the models for N content and water content varied in the range of 0.77–0.91 (0.25–0.14), and 0.53–0.95 (0.75–0.36), respectively, for single-date data, and 0.76–0.89 (0.24–0.15), and 0.65–0.92 (1.49–1.66), respectively, for combined-dates data. Performance of RF was better for combined-dates than single-date data. The conclusion is that a combination of digital image processing with an appropriate machine learning method has a high potential for plant growth status assessment in the field.
