Corn Leaf Disease Detection using Quantum CNN
Arepalli Peda Gopi, Sravya Chennamsetti, Mary Midhila Poluri +3 more
Corn leaf diseases are one of the biggest challenges undermining crop productivity and ensuring global food security, so early and accurate identification is crucial for proper management of the crop. Although Dense CNN and GoogleNet achieved high accuracies of 98.06% and 98.7%, respectively, the models fail to capture more complex relationships and patterns related to various leaf diseases because they use classical deep learning architectures. But with large datasets, the potential comes only in optimizing feature extraction and classification processes. With this, a need arises to introduce Quantum Convolutional Neural Networks (QCNN), where the principle of quantum computing can be utilized to enhance the representation of features for better detection accuracy. The demonstration of our experiments showed that the QCNN achieves a detection accuracy of 99%, superior to both Dense CNN and GoogleNet. Agriculture.
International Conference on Intelligent Sustainable Systems (ICISS)•Mar 12, 2025•3 citations