Nondestructive evaluation of leaf chlorophyll content by multiple regression analysis of RGB image components: a case study of Quinoa and Amaranth grown under different irrigation strategies (Comunicazione a convegno)

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Label
  • Nondestructive evaluation of leaf chlorophyll content by multiple regression analysis of RGB image components: a case study of Quinoa and Amaranth grown under different irrigation strategies (Comunicazione a convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Alternative label
  • Riccardi M.*, Mele G.*, Pulvento C.*, Lavini A*., d'Andria R.*, Jacobsen S.-E.** (2013)
    Nondestructive evaluation of leaf chlorophyll content by multiple regression analysis of RGB image components: a case study of Quinoa and Amaranth grown under different irrigation strategies
    in SWUP-MED PROJECT FINAL CONFERENCE - SUSTAINABLE WATER USE FOR SECURING FOOD PRODUCTION IN THE MEDITERRANEAN REGION UNDER CHANGING CLIMATE, Agadir (Marocco), 10-15 March, 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Riccardi M.*, Mele G.*, Pulvento C.*, Lavini A*., d'Andria R.*, Jacobsen S.-E.** (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Presented in \"Session V: \"Quinoa adaptation\" (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://swup-med.dk/meetings/final-conference/SWUPMED__Conference_Programme.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • *CNR- Institute for Agriculture and Forestry in the Mediterranean (ISAFoM)ff, Ercolano NA, Italy **Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Taastrup, Denmark. (literal)
Titolo
  • Nondestructive evaluation of leaf chlorophyll content by multiple regression analysis of RGB image components: a case study of Quinoa and Amaranth grown under different irrigation strategies (literal)
Abstract
  • Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. So its reduction often observed under water stress can be deleterious to the crop productivity. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination; this measurement, however, is expensive, laborious and time consuming. Over the years alternative methods noninvasive and more rapid have been explored. The aim of the present work was to evaluate the possibility to use the information contained in RGB digital images to develop a fast, inexpensive and non-destructive technique for chlorophyll estimation in Quinoa and Amaranth leaves. Digital images of leaves from different genotypes of Chenopodium quinoa and Amaranthus sp. were acquired by an SLR camera, during all development stages, after positioning them on a dark graduated plane support covered with a transparent sheet. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll values provided by standard laboratory procedure. As a comparison another widely used, nondestructive method (SPAD chlorophyll meter) was applied. The most appropriate regression models were discussed. For all genotypes analyzed SPAD readings resulted linearly and positively correlated to the total chlorophyll content while RGB components resulted singularly correlated by nonlinear models. Quinoa and Amaranth leaves showed the best correlation with the green and red color component values (RGB color model), respectively, with R2 values higher than SPAD (0.80-0.82 respect to 0.74-0.79). The multi regression model of all RGB components best accurately predicted total chlorophyll content respect to single regressions (R2 ranged from 0.82 to 0.87). The proposed RGB multiple regression model was then validated with data collected during a field experiment on two Quinoa and Amaranth genotypes grown under different irrigation regimes and showed its ability to follow, accurately, changes in the chlorophyll content of both genotypes for each irrigation treatments. The proposed leaf image analysis method provides a quick, nondestructive and reliable quantitative measure of the chlorophyll content offering better results than SPAD meter. (literal)
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