Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Archives for Technical Sciences

QUANTUM-ENHANCED DEEP LEARNING MODEL FOR CROP–WEED CLASSIFICATION IN SORGHUM IMAGERY USING THE SORGHUM CROP WEED DATASET CLASSIFICATION

J Justina Michael, R. Logeshwari, R. Radhika, P L Joseph Raj, M. S. Mahalakshmi

Published Sep 29, 2026• 0 citations
Publisher

Abstract

Classification of crops and weeds is an important aspect in precision agriculture, as it aids in providing a means for efficient weed control and reduced herbicide applications to achieve high crop productivity. In this paper, a quantum-classical hybrid deep learning approach is presented for accurate identification of crops and weeds from sorghum images using the dataset Sorghum Crop Weed Dataset_Classification. In this hybrid framework, a convolutional neural network (CNN) for feature extraction in the spatial domain, along with a variational quantum circuit (VQC) to provide better feature representation and classification abilities in higher-dimensional quantum space, is used. This allows for effective differentiation of crop and weed instances that appear alike in different agricultural conditions. Data preprocessing methods like resizing, normalization, and data augmentation (rotation, flipping, and brightness adjustment) are performed to ensure uniformity in data and increase the generalizability of the trained model. According to experimental evaluations conducted using traditional metrics, the designed model attains an accuracy of 97.3%, precision of 96.9%, recall of 97.2%, F1-score of 97.1%, and a training loss of 0.09. The results show a significant increase from baseline approaches such as CNN (92.5% accuracy), ResNet (94.0% accuracy), and transfer learning (95.0% accuracy). In addition, the designed model performs well when tested under challenging situations encountered in the real-world agricultural environment, such as varying illuminations, partial occlusion, and background noises. In summary, the above results prove the success of combining classical deep learning and quantum feature learning to enhance classification performance.

DOI
10.70102/afts.2026.1836.060

Related papers

Revolutionizing smart farming: Quantum computing applications in plant disease detection: A hybrid quantum-classical approach for sustainable agriculture

S. H. Lakhani

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.

International Journal of Computing Programming and Database Management•Jul 1, 2025•2 citations

Comparing classical and quantum machine learning approaches for crop and weed detection

Sneha Kandacharam, Poonam Chaudhary, Shriram K. Vasudevan

Abstract Crop and weed detection is an essential task in precision agriculture which is typically approached through classical machine learning, and more recently, through quantum machine learning (QML). This research compares classical and quantum approaches for crop-weed distinctions in aerial field photography. A convolutional neural network (CNN) stands as a classical baseline, and two QML models, a quantum support vector classifier and a variational quantum classifier (VQC) are utilized with a publicly accessible annotated dataset. The models are evaluated in terms of classification accuracy, inference velocity, and computational complexity. The CNN achieves a peak test accuracy of approximately 94%, which surpasses the QML approaches yielding 85%–89% accuracies. fiCNN achieves substantially faster inference, 10 ms per image and hundreds of milliseconds for quantum models due to circuit execution and simulation cost. While worse at predicting, QML models still consume considerably fewer trainable parameters (tens compared to over one million for the CNN), which may signal benefits on model compactness. The results show that classical deep learning is the most effective and reliable approach for crop and weed detection in 2025, while QML models represent a promising future direction and will become more competitive as the computing hardware and algorithms advance.

Engineering Research Express•Aug 1, 2026•0 citations

Hybrid Quantum Classical AI System to Detect High-accuracy Leaf Disease Recognition and Oesophageal Cancer Diagnosis

B. Ragavendran, S. Loganathan, P. Suresh +2 more

Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.

EPJ Web of Conferences•Jan 1, 2026•1 citation

Hybrid Quantum Classical AI System to Detect High-accuracy Leaf Disease Recognition and Oesophageal Cancer Diagnosis

B. Ragavendran, S. Loganathan, P. Suresh +2 more

Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.

•Mar 23, 2026•0 citations

Quantum-Enhanced DenseNet Architecture for Automated Plant Disease Recognition from Leaf Images

Vivek Kumar Sarathe and Amit Kumar Chandanan

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

International Journal of Advanced Research in Science Communication and Technology•Oct 18, 2026•0 citations

Transforming Crop Disease Classification with Hybrid Quantum Deep Learning

Muhammad Shoaib Sarwar, Komal Batool

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.

Horizons of Information Technology and Engineering (HITE)•Dec 12, 2025•0 citations
Classification of crops and weeds is an important aspect in precision agriculture, as it aids in providing a means for efficient weed control and reduced herbicide applications to achieve high crop productivity. In this paper, a quantum-classical hybrid deep learning approach is presented for accurate identification of crops and weeds from sorghum images using the dataset Sorghum Crop Weed Dataset_Classification. In this hybrid framework, a convolutional neural network (CNN) for feature extraction in the spatial domain, along with a variational quantum circuit (VQC) to provide better feature representation and classification abilities in higher-dimensional quantum space, is used. This allows for effective differentiation of crop and weed instances that appear alike in different agricultural conditions. Data preprocessing methods like resizing, normalization, and data augmentation (rotation, flipping, and brightness adjustment) are performed to ensure uniformity in data and increase the generalizability of the trained model. According to experimental evaluations conducted using traditional metrics, the designed model attains an accuracy of 97.3%, precision of 96.9%, recall of 97.2%, F1-score of 97.1%, and a training loss of 0.09. The results show a significant increase from baseline approaches such as CNN (92.5% accuracy), ResNet (94.0% accuracy), and transfer learning (95.0% accuracy). In addition, the designed model performs well when tested under challenging situations encountered in the real-world agricultural environment, such as varying illuminations, partial occlusion, and background noises. In summary, the above results prove the success of combining classical deep learning and quantum feature learning to enhance classification performance.
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.
Abstract Crop and weed detection is an essential task in precision agriculture which is typically approached through classical machine learning, and more recently, through quantum machine learning (QML). This research compares classical and quantum approaches for crop-weed distinctions in aerial field photography. A convolutional neural network (CNN) stands as a classical baseline, and two QML models, a quantum support vector classifier and a variational quantum classifier (VQC) are utilized with a publicly accessible annotated dataset. The models are evaluated in terms of classification accuracy, inference velocity, and computational complexity. The CNN achieves a peak test accuracy of approximately 94%, which surpasses the QML approaches yielding 85%–89% accuracies. fiCNN achieves substantially faster inference, 10 ms per image and hundreds of milliseconds for quantum models due to circuit execution and simulation cost. While worse at predicting, QML models still consume considerably fewer trainable parameters (tens compared to over one million for the CNN), which may signal benefits on model compactness. The results show that classical deep learning is the most effective and reliable approach for crop and weed detection in 2025, while QML models represent a promising future direction and will become more competitive as the computing hardware and algorithms advance.
Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.
Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.
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
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.