Predicting in-season crop yield is a unique tool for drawing important crop management decisions for precision farming. Field experiments were conducted at two locations in northwestern India under different agro-climatic zones to predict and validate spring maize yield using various in-season spectral indices. The spectral properties measured with leaf color chart (LCC), chlorophyll meter (SPAD meter), and GreenSeeker optical sensor were used to predict grain yield. A power function based on the Normalized Difference Vegetative Index (NDVI) measured with GreenSeeker optical sensor at V9 growth stage (9th leaf with fully exposed collar) presented higher values of coefficient of determination and explained 61% of the variability in spring maize grain yield, whereas NDVI measured at early and late growth stages were not reliable for the purpose. The spectral properties recorded with the SPAD meter and LCC rendered better grain yield estimates at VT growth stage (tasseling) and were respectively able to explain 75 and 76% variability in grain yield. The developed models were validated on an independent data set from another field experiment on spring maize. The normalized root mean square error (NRMSE) was <10% for LCC and SPAD at all the growth stages and at V9 growth stage for NDVI. The LCC, SPAD, and NDVI values adjusted with cumulative growing degree day were not helpful to improve NRMSE.

The evolution of a spherically symmetric cloud, continuously replenished from a Gaussian source with particle speed parameter σ, is characterized at time τ by a folded normal radial density with parameter στ and constant growth rates for p-percentile envelopes. Density functions for the speed of particles seen in profile by an optical sensor with a 4π steradian field of view at various times τ>0 and distances L ≦ ∞ are derived and related to the Rayleigh and Maxwell laws associated with i.i.d. N(0, σ) velocity components.