Enhancing classification capacity of CNN models with deep feature selection and fusion: A case study on maize seed classification

Abstract

Developing computer-assisted agricultural analysis systems using machine learning methods is a focused area in recent years. As in every field, it is aimed to improve the production process and product quality in agricultural applications with computer-assisted systems. The maize plant is a very important species in terms of providing sufficient food to the world’s population. The separation process of the haploid and diploid maize seeds is a critical issue in terms of maize breeding time and production efficiency. In this study, a haploid–diploid maize seed classification method is proposed using deep features obtained from convolutional neural networks (CNN). In the first stage, deep features were obtained from fully connected layers of different CNN models. Then, the best 100 features were selected by using the MRMR (Max-Relevance and Min-Redundancy) feature selection method for 1000 features obtained in each CNN model. These selected features have been fused according to different combinations of the CNN models. These fused features have been used in the training and testing stages of a conventional classifier method in the last stage. Eventually, according to the experimental results, it was determined that the general accuracy performance has been around 96.74%. It has been observed that the proposed approach exhibits high performance in the classification process of maize seeds.