Plants are susceptible to numerous illnesses during their growth stages, rendering early diagnosis a significant challenge in contemporary agriculture. Not being able to find infections early on can greatly lower agricultural yields and hurt farmers' incomes. There are several new methods that use Machine Learning and Deep Learning to solve this problem; however they generally have low classification accuracy or need millions of trainable parameters, which makes them less efficient. To overcome these limitations, this study proposes a Quantum Convolutional Neural Network (QCNN) model for the automated detection of plant diseases. To the best of our knowledge, there is currently no literature that has investigated QCNNs for this purpose. This work employs the proposed approach to detect Bacterial Spot disease in peach plants utilizing leaf photos from the publicly accessible Plant Village dataset. The system only needs 9,914 trainable parameters to work, and it gets 94.96 % accuracy. These results show that QCNNs are good at predicting plant diseases and could be useful in other areas of agriculture as well.
Plants are susceptible to numerous illnesses during their growth stages, rendering early diagnosis a significant challenge in contemporary agriculture. Not being able to find infections early on can greatly lower agricultural yields and hurt farmers' incomes. There are several new methods that use Machine Learning and Deep Learning to solve this problem; however they generally have low classification accuracy or need millions of trainable parameters, which makes them less efficient. To overcome these limitations, this study proposes a Quantum Convolutional Neural Network (QCNN) model for the automated detection of plant diseases. To the best of our knowledge, there is currently no literature that has investigated QCNNs for this purpose. This work employs the proposed approach to detect Bacterial Spot disease in peach plants utilizing leaf photos from the publicly accessible Plant Village dataset. The system only needs 9,914 trainable parameters to work, and it gets 94.96 % accuracy. These results show that QCNNs are good at predicting plant diseases and could be useful in other areas of agriculture as well.
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.
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.
Plants and plant-produced vegetables are crucial for every age group due to their rich nutrient content, and tomatoes, as one of the leading vegetables, make a significant impact on human well-being.Tomatoes can easily be affected by environmental conditions and fungal infections, causing significant losses for farmers. Experimentation of Quantum machine learning and image processing creates stunning results in early disease detection in medical field. This research takes this guidance and endeavors to apply various quantum machine learning classification techniques with optimization to classify diseased tomato leaves. Quantum Neural Network (QNN), Quantum Convolution Neural Network (QCNN), and Quantum-Classical Hybrid Convolution Neural Network (QCHCNN) with Quantum Approximate Optimization Algorithm (QAOA), COBOLYA (Constrained Optimization by Linear Approximation and Simultaneous Perturbation Stochastic Approximation (SPSA) were experimented for tomato leaf disease detection to identify a timely and low-cost model. These classifiers achieved accuracies of 87%, 90%, and 99% respectively. The results suggest Quantum-Classical Hybrid Convolution Neural Network (QCHCNN) as the preferred choice for tomato leaf disease detection.
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.
Potato leaf diseases pose a serious challenge to agricultural sustainability and food security in regions such as West Bengal, India, where potato cultivation plays a vital socio-economic role. Although classical deep learning models demonstrate strong performance, they often suffer from overfitting, limited generalization, and heavy reliance on large labeled datasets, while manual inspection remains time-consuming and error-prone. This work proposes a hybrid Quantum Convolutional Neural Network (QCNN) integrated with 4-qubit parameterized quantum circuits (PQCs) for accurate classification of five major potato leaf disease categories, including nutrient deficiency, fungal, and viral infections. The model incorporates quantum activation layers, quantum softmax, and a focused loss function to enhance non-linear feature representation and multi-class separability. Grad-CAM is employed to improve the interpretability of predictions. Experiments conducted on a diverse, field-collected dataset from Bardhaman, Bankura, and Purulia districts achieve a classification accuracy of 99.94%, demonstrating superior generalization, robustness, and explainability compared to classical approaches. This study highlights the practical potential of quantum-enhanced AI for precision agriculture.