Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #317292


Location: Hydrology and Remote Sensing Laboratory

Title: Comparison of leaf color chart observations with digital photographs and spectral measurements for estimating maize leaf chlorophyll content

item Friedman, J.m. - Collaborator
item Hunt, Earle - Ray
item Mutters, R.g. - University Of California

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/24/2016
Publication Date: 1/4/2016
Publication URL:
Citation: Friedman, J. M., Hunt, E.R., Mutters, R.G. 2016. Comparison of leaf color chart observations with digital photographs and spectral measurements for estimating maize leaf chlorophyll content Agronomy Journal. 108:473-480.

Interpretive Summary: Non-destructive measurements of crop nitrogen status are important for managing fertilizer application rates, but often field sizes are small and low cost methods are required. Leaf color charts (LCCs) were originally designed for rice nitrogen management in Southeast Asia, and were shown to be effective managing nitrogen in other crops such as maize and wheat. LCCs have a series of panels from yellow-green to dark green that were designed to have the same visual appearance as leaves with different amounts of chlorophyll. The number of the panel that matches leaf color indicates whether the plants have insufficient, sufficient, or excess nitrogen. The main problem with LCCs is that the selection of the panel closest in color to a leaf is a subjective decision based on human visual interpretation. We hypothesized that photographs from digital color cameras could be used to make objective, reproducible and potentially-automated selections of the LCC panel with the color closest to a leaf. We acquired maize leaves from on ongoing nitrogen fertilization experiment, and LCC panel numbers were highly correlated with measured leaf chlorophyll contents and measured chlorophyll meter values. We predicted the LCC panel number from two methods of supervised classification (minimum distance and spectral angles) and two methods based on spectral vegetation indices (dark green color index and triangular greenness index). Minimum distance classification was successful using either JPEG photographs or raw camera files. Both spectral indices produced their best results with the raw camera files. However, using a digital camera with the LCC did not improve accuracy in estimating chlorophyll content compared to the visual interpretation. Leaf color charts may be a low-cost method for managing nitrogen fertilizer for use in lawns and gardens, and digital photographs with the LCC may be used to show compliance with local environmental policies.

Technical Abstract: Crop nitrogen management is important world-wide, as much for small fields as it is for large operations. Developed as a non-destructive aid for estimating nitrogen content in rice crops, leaf color charts (LCC) are a numbered series of plastic panels that range from yellowgreen to dark green. By visual comparison, the panel closest in color to a leaf indicates whether nitrogen is deficient, sufficient, or in excess. Because the selected values depend on subjective decisions by an observer, our goal was to determine whether spectral reflectances or digital color photographs could provide an objective, reproducible and potentially-automated method for determining LCC values. Maize leaves (Zea mays L.) were collected on two dates from an ongoing nitrogen fertilization experiment. Spectral reflectances,Chlorophyll meter values, digital photographs, and leaf Chlorophyll contents were measured for the collected leaves. observed LCC values were highly correlated to chlorophyll content and SPAD values. Supervised classifications of the digital photographs using minimum distance provided reasonable predictions of LCC value of a given leaf, but the spectral angle mapper did not. The dark green color index and the triangular greenness index also provided good predictions of LCC panel value. Uncorrected RAW images produced better agreement compared to JPEG photographs when using spectral indices; however JPEG photographs produced better agreements using the supervised classification methods. We concluded that subjective visual observations using a leaf color chart were not worse than the objective methods. Because leaf color methods estimate chlorophyll content rather than nitrogen status, calibrations are still required for different crops and soils. However, if nitrogen status was related to chlorophyll content, then the same data could be used to provide calibrations for a variety of remote and proximal sensors.