The present study proposes a novel optimization framework for tomato leaf disease detection, combining Quantum Vision Transformers (QViT) with a hybrid optimization strategy. The global parameter search utilizes quantum annealing, q-PSO for feature selection, and an enhanced Adam optimizer with quantum momentum for fine-tuning, consequently greatly reducing model convergence time. This new algorithm exploits quantum superposition states to visit many different parameter configurations at the same time, while a customized entropy-based loss function guides optimization towards the best accuracy in disease detection. With 50,000 tomato leaf images in the test dataset, experimental results suggest this hybrid approach performs better than conventional optimization methods: it achieves 98.5% accuracy when identifying four major tomato diseases (early blight, late blight, leaf mold and mosaic virus) and reduces training time by almost two-thirds. The system keeps working well in many different environmental states and periods for the growth of tomato plants. This sets a new benchmark for efficient and accurate plant disease detection.
The present study proposes a novel optimization framework for tomato leaf disease detection, combining Quantum Vision Transformers (QViT) with a hybrid optimization strategy. The global parameter search utilizes quantum annealing, q-PSO for feature selection, and an enhanced Adam optimizer with quantum momentum for fine-tuning, consequently greatly reducing model convergence time. This new algorithm exploits quantum superposition states to visit many different parameter configurations at the same time, while a customized entropy-based loss function guides optimization towards the best accuracy in disease detection. With 50,000 tomato leaf images in the test dataset, experimental results suggest this hybrid approach performs better than conventional optimization methods: it achieves 98.5% accuracy when identifying four major tomato diseases (early blight, late blight, leaf mold and mosaic virus) and reduces training time by almost two-thirds. The system keeps working well in many different environmental states and periods for the growth of tomato plants. This sets a new benchmark for efficient and accurate plant disease detection.
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.
This study addresses the critical need for accurate and interpretable plant disease detection in sustainable agriculture. Existing deep learning models often suffer from limited generalization across crops, insufficient modeling of complex feature interactions, and a lack of explainability, restricting their real-world applicability. To overcome these challenges, this work proposes TIGLeafNet-Q, a hybrid quantum–classical framework for multi-crop leaf disease detection. The proposed approach integrates an InceptionV3 transfer learning backbone for deep feature extraction, followed by feature compression and angle encoding into quantum states. A variational quantum circuit (VQC) is employed to capture higher-order correlations among disease features, and Grad-CAM is used to provide visual interpretability. The model is evaluated on large-scale tomato (22,980 images) and cotton (2,204 images) datasets. Experimental results demonstrate classification accuracies of 99.28% (tomato) and 97.23% (cotton), outperforming existing methods. The proposed framework contributes a scalable, explainable, and multi-crop capable solution, highlighting the potential of hybrid quantum–classical learning in agricultural intelligence systems.
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.
Early and accurate diagnosis of plant diseases is super important for boosting crop productivity and cutting down on economic losses. Deep learning models have really nailed image-based disease detection lately, but they face some hurdles like complex feature distributions and class imbalance in ag data sets. This study takes a neat approach to solve those issues – a hybrid quantum-classical deep learning framework. It combines a pretrained DenseNet121 with a Variational Quantum Circuit (VQC). The DenseNet121 grabs key visual features from leaf images, while the quantum part does fancy nonlinear feature transformations in a high-dimensional quantum space. To make the model tougher and more adaptable, the researchers used Mixup augmentation, label smoothing, and balanced sampling when training it. Their tests on the Plant Pathology dataset scored an 89% accuracy overall, with precision, recall, and F1-score coming in at 88%, 87%, and 88%, respectively. So it seems hybrid quantum-classical learning could seriously beef up plant disease classification and maybe even pave the way for smarter ag diagnostic tools in the future
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.
Plant leaf diseases are a critical threat to global food security, with early detection complicated by high inter-class similarity, environmental variations, and the demand for scalable multiclass classification across diverse crops. To address these challenges, we propose P-HQNN, a lightweight yet robust 4-qubit Hybrid Quantum–Classical Convolutional Neural Network designed for near-term quantum devices. The model uses EfficientNet-B0 to extract deep feature, and Particle Swarm Optimization (PSO) with Random Forest ranking is used to reduce the 1280-dimensional embeddings into four highly discriminative features that can be represented using quantum encoding. These features are next coded as a 4-qubit quantum circuit of ZZFeatureMap and RealAmplitudes ansatz and a hybrid quantum layer is formed whose outputs are concatenated with a CNN classifier to arrive at final predictions. This optimization strategy of features representation based on quantum awareness balances high-dimensional representation and qubit constraints, and is proved to be more stable in 4-qubit encoding and better in comparison to higher-qubit encoding under noisy conditions. Experimental evaluation on the “New Plant Diseases Dataset” achieved 98.2% accuracy, 98.5% precision, 97.7% recall, 98% F1-score, and a macro ROC-AUC of 0.985, confirming that low-qubit hybrid models can deliver state-of-the-art plant disease classification while ensuring practicality and scalability for real-world agricultural applications on near-term quantum computing platforms.