This study introduces a novel Quantum Recurrent Neural Network (QRNN) architecture for plant disease detection, achieving 98.8% classification accuracy across 38 disease categories in 12 crop species. The hybrid quantum-classical model combines a CNN-based feature extractor with a 4-qubit parameterized quantum circuit integrated into a recurrent structure. Compared to classical approaches, our QRNN demonstrated 15.3% higher accuracy than traditional CNNs (79.5%) and 12.7% improvement over classical RNNs (82.1%) when tested on 25,000 temporal image sequences. The model exhibited superior early detection capabilities, identifying disease signatures an average of 3.2 days earlier than conventional methods. Notably, the QRNN achieved 91.2% accuracy in detecting subtle disease progression patterns during early stages, compared to 76.8% for classical approaches. Computational analysis revealed a 2.8× speedup in training time using quantum-enhanced processing on IBM’s 16-qubit quantum computer. These results demonstrate the significant potential of quantum-enhanced neural networks in agricultural disease monitoring, particularly for early-stage detection and complex temporal pattern recognition.
This study introduces a novel Quantum Recurrent Neural Network (QRNN) architecture for plant disease detection, achieving 98.8% classification accuracy across 38 disease categories in 12 crop species. The hybrid quantum-classical model combines a CNN-based feature extractor with a 4-qubit parameterized quantum circuit integrated into a recurrent structure. Compared to classical approaches, our QRNN demonstrated 15.3% higher accuracy than traditional CNNs (79.5%) and 12.7% improvement over classical RNNs (82.1%) when tested on 25,000 temporal image sequences. The model exhibited superior early detection capabilities, identifying disease signatures an average of 3.2 days earlier than conventional methods. Notably, the QRNN achieved 91.2% accuracy in detecting subtle disease progression patterns during early stages, compared to 76.8% for classical approaches. Computational analysis revealed a 2.8× speedup in training time using quantum-enhanced processing on IBM’s 16-qubit quantum computer. These results demonstrate the significant potential of quantum-enhanced neural networks in agricultural disease monitoring, particularly for early-stage detection and complex temporal pattern recognition.
This paper presents a novel approach to crop disease classification through the integration of quantum computing principles with deep learning techniques. We introduce a hybrid quantum neural network architecture that leverages quantum mechanical phenomena, including superposition and entanglement, to enhance crop disease detection capabilities. Using a comprehensive dataset of 23,265 images spanning 19 disease classes across five major crop species, our model achieved a test accuracy of 0.7958 with a weighted average F1-score of 0.85. The architecture combines classical convolutional layers for initial feature extraction with quantum processing units for advanced pattern recognition, demonstrating significant potential in the diagnosis of agricultural diseases. Our findings establish the viability of quantum-enhanced neural networks in agricultural applications, while highlighting pathways for future development in quantum machine learning systems.
This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.
Accurate image classification is vital in smart agriculture, where early detection of plant diseases can boost crop yield and sustainability. While traditional Convolutional Neural Networks (CNNs) perform well, they face computational and scalability issues as dataset sizes grow. To address this, we propose a quantum-enhanced deep learning framework that combines the Quantum Fourier Transform (QFT) for image preprocessing with a Quantum Convolutional Neural Network (QCNN) for classification. QFT transforms images into the frequency domain, highlighting disease-related features and reducing background noise, while QCNN leverages quantum principles like superposition and entanglement for faster and more efficient feature learning. We evaluated four architectures—classical CNN, QCNN, CNN+QFT, and the hybrid QCNN+QFT—on coffee leaf datasets containing 2,000, 5,000, and 12,000 images. Across all dataset sizes, QCNN+QFT consistently achieved the highest accuracy and lowest training times. On the largest dataset, it reached 98.87% accuracy in 25.46 seconds per epoch, compared to the CNN’s 85.13% in 638.39 seconds. For 5,000 images, it achieved 94.78% in 11.30 seconds, outperforming CNN’s 83.42% in 215.57 seconds. Even on 2,000 images, it scored 90.06% in 2.12 seconds versus CNN’s 79.23% in 67.48 seconds. While QCNN and CNN+QFT also improved over the classical CNN, neither matched the hybrid’s superior balance of speed and accuracy, demonstrating its potential for scalable, real-time plant disease detection.
Detecting plant diseases is a crucial component of precision agriculture, necessary for maintaining economic viability, sustainable farming practices, food security, and preventing economic losses. Deep learning models such as Capsule Attention Networks, DenseNets, and Convolutional Neural Networks have demonstrated state-of-the-art performance in leaf-image-based disease classification. However, large-scale practical implementation in resource-constrained environments remains challenging due to overfitting, environmental insensitivity, extended training times, and high computational cost, along with the absence of a sustainable framework for real-world deployment. Emerging quantum neural networks (QNNs), integrated with quantum-influenced optimization algorithms, offer promising avenues to enhance practical applicability. Their capabilities for high-dimensional feature mapping, entanglement, and superposition improve generalization and convergence in hybrid models that combine quantum layers with classical architectures. Incorporating quantum modules into DenseNets, attention mechanisms, capsule networks, and other hybrid systems may further enhance accuracy, enable robust disease segmentation, and reduce training time. Nonetheless, challenges remain regarding real-time deployment on edge devices, efficient data encoding, limited explainability of quantum-driven features, and hardware constraints. This survey reviews classical, hybrid, and quantum-enhanced approaches, provides comparative insights, and identifies research gaps toward developing scalable, interpretable, and dependable frameworks for plant disease detection and diagnosis.
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.
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.