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

International conference Computer Networks and Inventive Communication Technologies

Quantum Inspired Graph Neural Network for Robust and Parameter Efficient Multi Crop Plant Disease Diagnosis

N. Hariprasad, Prof. Deepa P, Dhanush G, Stalin Rayappan S

Published May 11, 2026• 0 citations
Publisher
DOI
10.1109/iccnct68477.2026.11590192

Related papers

Quantum Convolutional Neural Networks for Enhanced Crop Disease Detection and Management: A Step Towards Intelligent Farming Systems

Shiplu Das, Debdutta Pal, Debojyoti Bhuinya +3 more

Lecture notes in networks and systems•Nov 22, 2025•1 citation

Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection

Musa Dima Genemo

Lecture notes in networks and systems•Jan 1, 2023•8 citations

Para-QNet: Parallel Quantum Circuits for Scalable Edge AI in Plant Disease Diagnosis

Ghanapriya Singh, Deepak Chauhan, Harshit Kumar

International Conference in Electronic Engineering & Information Technology (EEITE)•Jun 1, 2026•0 citations

Liver Disease Diagnosis Using Quantum-based Binary Neural Network Learning Algorithm

Om Prakash Patel, Aruna Tiwari

Advances in intelligent systems and computing•Dec 23, 2014•5 citations

Quantum Convolutional Neural Network Framework for Efficient and Accurate Plant Disease Diagnosis

Gumpula Jhansi, Polasi Sudhakar, Chennam Chandrika Surya +3 more

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.

International Conference on Electronic Circuits and Signaling Technologies (ICECST)•Oct 23, 2025•3 citations

Quantum Convolutional Neural Network for Coffee Plant Disease Diagnosis

Savitri Kulkarni, S. G. S. Rajeshreddy V., P. Deepa Shenoy +1 more

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.

IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)•Sep 26, 2025•0 citations
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.