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.
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.
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.
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.
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.
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.
The main limitations of agricultural productivity are diseases and shortage of micro-nutrients especially in vegetables like bitter gourd. It is of essence that such stresses are detected early in an attempt to maximize the yield so that farming activities can find a place within the sustainability of the environment. The dataset that we have used in this paper is the image dataset of an olive leaf namely OLID-I subset of bitter gourd leaves that has a total of 785 high-resolution images divided into nine different classes, namely, healthy, disease, insect attack and nutrient deficiencies. Note, as the baseline, we use AgriCure-YOLOv8 model, trained on layered augmentations, with reported good performance of mAP score of 92.9% and a precision of 89.6% and a recall of 86.6%. Therefore, in our work we have QML algorithms in mind on leaf stress classification and using the result offered by the YOLOv8 baseline as the starting point we carry out scale-up analysis. Three models are compared and contrasted; the Quantum Convolutional Neural Network (QCNN) which is a Hierarchical Quantum Feature Extraction, the Variational Quantum Classifier (VQC) which is a direct quantum classification and a Hybrid Quantum-Classical Classifier (HQ-CC), in which the classical pre-classification is performed before the quantum classification. The obtained findings indicate that the quantum models are workable and can be deployed to handle small but complicated agronomic data and yield benefits in the realms of generalization performance and feature representations. In comparison to the YOLOv8 baseline accuracy, the paper presents quantum models, where the additional performance is demonstrated on identifying nutrient deficiencies, which is the most significant outcome that will be utilized in entering this new paradigm technology of smart agriculture, and in resource-poor environments, in particular, where plant stress is of paramount importance.In contrast to the current deep learning-based systems like the YOLOv8, the proposed quantum models have a higher generalization capability based on small and complex dataset and higher sensitivity to detect nutrient deficiencies. This means that QML is applicable in a resource-constrained agricultural setting.
Reduced crop losses and guaranteeing agricultural productivity depend on the early and accurate detection of plant diseases. This work offers a Hybrid Quantum-Classical HQC-CNN, or convolutional in nature neural network that uses image analysis to determine various diseases of apple leaves. The model is executed using the PennyLane a system which blends quantum computing techniques with conventional deep computing methods. In the classical segment, layers for collaboration and convolution were used to extract significant spatial features from normalised and resized leaf images.These features are mapped into quantum states by Angle Embedding, and intricate patterns are revealed by StronglyEntanglingLayers processing them inside an electromagnetic circuit. The quantum information layer's output is sent to a softmax classifier, which divides the input into different disease groups using Pauli-Z expectation value measurements. The framework has been examined and developed with a publicly available dataset of apple leaf images, and it demonstrated strong performance on several evaluation parameters, such as the F1-s remembering, reliability, and quality. These findings show how well the model can identify a range of leaf diseases and point to how it could help precision farming and the broader field of quantum improved neural networks.