A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton

Rotylenchulus reniformis is a newly emerging nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the nematode population in the field to be known, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence there is a need to develop alternative methods through which the actual numbers of reniform nematode present in the field can be determined. In this paper we propose a methodology in which a canopy reflectance model (PROSAIL) is inverted using machine learning approaches to retrieve the biophysical parameters, and relate the key variables to the nematode levels, so that it is possible to quantify at all multi-temporal intervals the nematode infestation at geographically distributed fields. A Support Vector Machine (SVM) Regression method is used for the inversion and retrieval of key biophysical parameters which help to understand and quantify the nature of the nematode infested vegetation. The performance of this approach is analyzed by the accuracy measures of RMSE and N-fold cross validation average on a considerable data set. Finally, a graphical web portal is being developed to facilitate the end users to use their field collected data to determine the extent of the nematode infestation in their crop and retrieve other spatio-temporal statistics.