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Lecture notes in networks and systems

Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection

Musa Dima Genemo

Published Jan 1, 2023• 8 citations
Publisher
DOI
10.1007/978-981-99-7093-3_15

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In modern agriculture, high crop yields and food security depend on early and accurate identification of plant diseases. Deep learning (DL) models like convolutional neural networks (CNNs) have successfully classified plant disease using leaf images. Classical models are adequate for small-scale applications but inefficient for large-scale applications because of their high processing requirements and difficulty in handling highdimensional data. To overcome these challenges, this study proposes the Quantum Convolutional Neural Network (QCNN) by leveraging the Cirq library to introduce quantum circuit simulation. The QCNN model is trained on the Kaggle coffee plant dataset and JMuBEN Mendeley. Quantum Fourier transforms (QFT) are used to preprocess the input images for enhanced quality and to find regions of interest (ROI) for disease lesions. The empirical results prove that the QCNN has drastically improved the model's performance in each phase of the epoch when compared with the traditional CNN model. In the training phase, the QCNN accomplished 96.98 %, whereas the CNN achieved 88.95 % for 150 epochs. Similarly, an effective performance is accomplished during the testing phase by QCNN with 95.89% in comparison with the CNN performance of 85.77 %. Hence, this work emphasizes the power of QCNN in faster training and analytical capabilities using entanglement and superposition in the quantum domain.