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Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs
- Friedman, Jennifer M., Hunt, E. Raymond, Mutters, Randall G.
- Agronomy journal 2016 v.108 no.2 pp. 822-829
- Oryza sativa, Zea mays, automation, chlorophyll, color, corn, correlation, crops, digital images, image analysis, leaves, nitrogen content, nitrogen fertilizers, nutrient deficiencies, photographs, prediction, rice, spectroscopy
- Developed as a nondestructive aid for estimating the N content in rice (Oryza sativa L.) crops, leaf color charts (LCCs) are a numbered series of plastic panels that range from yellow-green to dark green. By visual comparison, the panel value closest in color to a leaf indicates whether N 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 (Zea mays L.) leaves were collected on two dates from an ongoing N fertilization experiment. Observed LCC panel values of selected leaves were highly correlated to chlorophyll content and chlorophyll meter values. Spectral reflectances and digital photographs were analyzed to predict the LCC panel value that was observed for each leaf. Supervised classifications of digital photographs using minimum distance provided reasonable predictions of the LCC value, but the spectral angle mapper did not. The dark green color index and the triangular greenness index predicted LCC panel values well. Uncorrected digital photographs (raw) produced better agreement with observed LCC panel values when using spectral indices, whereas color-corrected photographs (JPEG) produced better agreement using the supervised classification methods. We concluded that subjective visual observations using a LCC were not worse than the objective methods for estimating leaf chlorophyll content. Visual observations using LCCs may be an easy-to-use and low-cost method for managing N at smaller scales.