Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing

Abstract

Hop (Humulus lupulus L.) is a climbing plant that contains essential components for beer production. Although Brazil is the third-largest beer producer in the world, it still relies on imports to meet demand. Some hop varieties have already adapted to the tropical climate, but nitrogen fertilization is essential for the proper development of plants. Digital image processing (DIP) and modeling technologies are emerging as fast and economical alternatives for monitoring the nutritional status of plants. This study evaluated the impact of image quality and the performance of models in classifying hop plants in terms of nitrogen concentration, using predictors extracted from leaf images. A total of 24 plants subjected to six levels of fertilization, ranging from 0 to 200% of the optimal level, were analyzed. The leaves were classified into two nitrogen concentration groups and the data organized into two sets: one containing only significant variables and another including all the variables in the model. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) models were estimated. The QDA models demonstrated great efficacy in classifying plants with a high nitrogen concentration, achieving over 80% accuracy, although performance was lower for plants with a lower nitrogen concentration.