Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach

Altuntas Y., Comert Z., KOCAMAZ A. F.

COMPUTERS AND ELECTRONICS IN AGRICULTURE, cilt.163, 2019 (SCI-Expanded) identifier identifier

Özet

Maize is one of the most significant grains cultivated all over the world. Doubled-haploid is an important technique in terms of advanced maize breeding, modern crop improvement and genetic programs, since this technique shortens the breeding period and increases breeding efficiency. However, the selection of the haploid seeds is a major problem of this breeding technique. This process is frequently conducted manually, and this unreliable situation leads to loss of time and labor. Inspired by the recent successes of deep transfer learning, in this study, we approached this problem as a computer vision task to provide a nondestructive, rapid and low-cost model. To achieve this objective, we adopted convolutional neural networks (CNNs) to recognize haploid and diploid maize seeds automatically through a transfer learning approach. More specifically, AlexNet, VVGNet, GoogLeNet, and ResNet were applied for this specific task. The experimental study was carried out using a new dataset consisting of 1230 haploid and 1770 diploid maize seed images. The samples in the dataset were classified considering a marker-assisted selection, known as the R1-nj anthocyanin marker. To measure the success of the CNN models, we utilized several performance metrics, such as accuracy, sensitivity, specificity, quality index, and F-score derived from the confusion matrix and receiver operating characteristic curves. According to the experimental results, the CNN models ensured promising results, and we achieved the most efficient results via VGG-19. The accuracy, sensitivity, specificity, quality index, and F-score of VGG-19 were 94.22%, 94.58%, 93.97%, 94.27%, and 93.07%, respectively. Consequently, the experimental results proved that CNN models can be a useful tool in recognizing haploid maize seeds. Furthermore, we conclude that this approach is significantly superior to machine learning-based methods and conventional manual selection.