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. (July 2015)

• Record Type: Journal Article
• Authors: Confalonieri, Roberto; Paleari, Livia; Movedi, Ermes; Pagani, Valentina; Orlando, Francesca; Foi, Marco; Barbieri, Michela; Pesenti, Michele; Cairati, Oliver; La Sala, Marco S.; Besana, Riccardo; Minoli, Sara; Bellocchio, Eleonora; Croci, Silvia; Mocchi, Silvia; Lampugnani, Francesca; Lubatti, Alberto; Quarteroni, Andrea; De Min, Daniele; Signorelli, Alessandro; Ferri, Alessandro; Ruggeri, Giordano; Locatelli, Simone; Bertoglio, Matteo; Dominoni, Paolo; Bocchi, Stefano; Sacchi, Gian Attilio; Acutis, Marco
• Is Part Of: Biosystems engineering. Volume 135(2015:Jul.)
• Page Start: 21
• Page End: 30
• Publication Date: 2015-07
• Subjects: Dualex -- Leaf colour chart -- Management support -- PocketN -- Rice -- SPAD; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2015.04.013
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract: Operational tools to support nitrogen (N) management in cropping systems are increasingly needed to maximise profit, minimise 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 Dualex 4). 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 (R 2 = 0.96 and 0.92 for leaf and plant N contents), although it 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 (R 2 = 0.82 and 0.81 for leaf and plant N contents). 18%-grey DGCI was ranked second for trueness (R 2 = 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 (R 2 = 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 characterised by low availability of resources. Highlights: A new method for plant N content estimates based on image processing is presented. It was tested for rice and compared with other methods and commercial devices. Dualex was the method with the best trueness, SPAD the one with the best precision. The method proposed here is very cheap and presented a satisfying reliability.