Hybrid Quantum Neural Network for Rice Leaf Disease Classification

Indonesia is an agricultural country that was once listed as the third largest country in terms of rice production. Several factors, such as pests and diseases, are factors in Indonesia's decline in rice productivity. This research explores the integration of quantum computing into classical convolutional neural networks (CNN) for the classification of rice leaf diseases. The study utilizes a hybrid quantum neural network (HQNN) model by combining classical CNN with quantum circuits to evaluate the effect of quantum computing in enhancing agricultural disease detection. The dataset comprises 5932 images of rice leaves affected by four distinct diseases, with each image standardized to 300×300 pixels. The HQNN models were benchmarked against traditional CNN, significantly improving classification accuracy. HQNN reached the best training accuracy of 99.93% and validation accuracy of 99.83%. These findings show that quantum computing has great potential in terms of performance, especially when quantum hardware becomes more accessible. The research concludes by suggesting the need for further exploration with more extensive and diverse datasets to realize the benefits of HQNN in diverse agricultural applications fully.