Visually Assessing Maize Leaves: From Spectral Sampling to High-Fidelity Color Reproduction

Maize (Zea mays L., corn) crops are extensively used in food and biofuel production worldwide. A number of protocols have been proposed to use leaf color as an indicator of the health status of maize plants. Color perception is a complex process, however. The correct interpretation of its outcomes depends on several aspects. Accordingly, a variety of spectral vegetation indices have also been proposed to monitor the development of these plants. These indices usually require a number of spectral reflectance and transmittance samples taken from selected specimens using specialized sensors. Since these radiometric quantities do not depend neither on the spectra of the light sources nor on the physiological characteristics of the human visual system, these indices are not subject to color perception issues. The visual feedback provided by the chromatic attributes of plant leaves, on the other hand, can enable a broader assessment of the net effect of several environmental factors affecting an entire maize crop. Also, these attributes can be obtained using spectral reflectance and transmittance samples already employed in the computation of the aforementioned indices. These aspects indicate the potential benefits of the combined use of vegetation indices and leaf chromatic attributes in the monitoring of maize crops. Ideally, one would like to employ a number of spectral samples that would maximize the color fidelity to sensor costs ratio. In this paper, we address this practical trade-off. More specifically, using hyperspectral reflectance and transmittance data for maize specimens, we performed colorimetric experiments to obtain a lower bound for the number of spectral reflectance and transmittance samples sufficient to achieve a high degree of fidelity in the reproduction of maize leaves' colors under distinct illumination conditions.