Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Analysis of agricultural products is an important area that is widely emphasized today. In this context,with the development of technology, computer-aided analysis systems are also being developed. In thisstudy, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trainedconvolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG16 pre-trained models have been used as feature extractors for the haploid and diploid seed classificationprocess. In the first stage, the deep features of haploid and diploid maize seeds have been obtained inthese models. The features have been taken from different layers of network architecture. Instead ofsoftmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor,and support vector machine have been used. According to the classification results with these features, theachievements in network architectures and classifier methods have been observed. The experiments havebeen carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images.The experimental results revealed that the developed classification systems demonstrate a remarkableperformance.

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