Multiple deep learning by majority-vote to classify haploid and diploid maize seeds

The maize plant is a crucial global staple, integral to food security. To ensure sustainable maize production, the development of high-yielding and resilient maize varieties is essential. This study proposes a majority voting-based decision support system for classifying haploid and diploid maize seeds using deep features from Convolutional Neural Networks (CNNs). Key variables include the accuracy, sensitivity, specificity, F-score, and Matthew's correlation coefficient (MCC) of the classification models. Experimental results showed impressive performance with accuracy, sensitivity, specificity, F-score, and MCC values of 90.96 %, 94.53 %, 86.40 %, 92.15 %, and 81.96 %, respectively. These results underscore the efficiency of the proposed method in accurately distinguishing between haploid and diploid seeds. The implementation of this decision support system in agricultural practices can significantly reduce the …