Machine Learning Driven Model Inversion Methodology To Detect Reniform Nematodes In Cotton

Abstract

Rotylenchulus reniformis is a 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 intra-field variable nematode population, 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 estimating the nematode infestation on the cotton crop using remote sensing and machine learning techniques which are cost and time effective is the motivation for this study. In the current research, the concept of multi-temporal remote sensing has been implemented in order to design a robust and generalized Nematode detection regression model. Finally, a user friendly web-service is created which is gives trustworthy results for the given input data and thereby reducing the nematode infestation in the crop and their expenses on nematicides.