Improving in vivo plant nitrogen content estimates from digital images: trueness and precision of a new approach as compared to other methods and commercial devices

Operational tools to support nitrogen (N) management in cropping systems are increasingly needed to maximize profit, minimize environmental impact, and to cope with market requirements. In this study, a new method (18%-grey DGCI) for estimating leaf and plant N content from digital photography was evaluated and compared with others based on image processing (DGCI and Corrected DGCI) and with commercial tools (leaf colour chart, SPAD-502, and Dualex4). All methods were evaluated for rice using data collected in northern Italy in 2013, by adapting the ISO 5725-2 validation protocol. 18%-grey DGCI was further validated on independent data collected in 2014. Dualex achieved the best performances for trueness (R2 = 0.96 and 0.92 for leaf and plant N contents), although presented partly unsatisfying values for precision (12.33% for repeatability and 14.81% for reproducibility). SPAD, instead, demonstrated the highest precision (repeatability = 4.51%, reproducibility = 4.98%), even if it was ranked third for trueness (R2= 0.82 and 0.81 for leaf and plant N contents). 18%-grey DGCI was ranked second for trueness (R2 = 0.83 for both leaf and plant N contents) and third for precision (11.11% and 14.47% for repeatability and reproducibility). The good performances of the new method were confirmed during the 2014 experiment (R2 = 0.87 for leaf N content). The 18%-grey DGCI method has been implemented in a smartphone app (PocketN) to provide farmers and technicians with a low-cost diagnostic tool for supporting N management at field level in contexts characterized by low availability of resources.