Deepmaizenet: a Novel Hybrid Approach Based on Cbam for Implementing the Doubled Haploid Technique

Abstract

Maize (Zea mays L.) is an important cereal plant in the family of wheatgrass cultivated all over the world. With the increase in human population and environmental factors, the need for maize plants is increasing day by day. One of the efficient methods of increasing production of the maize is maize breeding. The most effective and rapid method for maize breeding is the doubled haploid (DH) technique. This technique reduces maize breeding time and increases productivity. There are different selection methods to select haploid maize seeds in the maize breeding process. Among these selection methods, the most common and most successful selection method is the visual checking of the R1-Navajo marker. Maize seed separation by hand is a time-consuming and error-prone operation. It is labor-intensive and very tiring; therefore, it is essential to develop a fast and highly accurate intelligent system that separates diploid and haploid maize seeds from each other. This study presents a pioneering approach, introducing the DeepMaizeNet, a hybrid deep learning model that showcases its prowess in accurately classifying haploid maize seeds. The classification of haploid seeds holds significant value for the DH technique, and the proposed model's success is a promising step toward enhanced efficiency. The proposed hybrid model exploits some new techniques such as convolution block attention module, hypercolumn, 2D upsampling, and residual block. For the assessment of the proposed model, the five-fold cross-validation technique is employed. The result shows that DeepMaizeNet provides a promising performance by achieving 94.13% accuracy, 94.91% F1-score, and 97.27% sensitivity.