Hyperparameter Optimization in Convolutional Neural Networks for Maize Seed Classification

Abstract

Corn farming is of great importance for the continuity of our society. Because corn is a cheap and efficient food, especially for animal feeding. However, with the Doubled-haploid technique, the selection of the haploid seeds necessary for this job to be done efficiently creates a problem. Today, the selection of haploid seeds is usually done by trained technicians. With the development of machine learning methods, the parts expected from technicians can be made by machines. In this study, a new model architecture based on a convolutional neural network (CNN) was produced to perform the selection of haploid seeds and the hyperparameters of this model were optimized with the use of tree-structured parzen estimator algorithm. The newly produced model achieved a 94.66% validation score, higher than the VGG-19 model, which proved to be relatively efficient.