Classification of Growth Conditions in Paprika Leaf Using Deep Neural Network and Hyperspectral Images

Abstract

Recently, the analysis research of crop's growth condition is done with the use of hyperspectral image. However, there are many factors such as physical factors and complexity of data make the hyperspectral image analysis difficult. This study presents the classification method of crop's leaf growth condition using hyperspectral image(HSI) and Deep Neural Network(DNN). Major information of plants is acquired through hyperspectral image, and the preprocessing is followed for the information to be used for DNN learning. The preprocessing is used by cutting the data in small patch size and rotating it for the models to be operated effectively. In the experiment, paprika leaves are divided into four types of leaves and backgrounds such as normal and damaged by harmful insects, and the result of the experiment showed 90.9% of accuracy. The presented method has advantages that the data generation method does not affect DNN and can classify various growth conditions that are difficult in the existing RGB image.