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@article{181631,
        author = {Dr. Jyoti Agarwal and Anu Saxena},
        title = {Next-Gen Plant Pathology: Quantum AI for Tomato Leaf Disease Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4942-4945},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181631},
        abstract = {Tomato crops are highly susceptible to various leaf diseases that can significantly impact yield and quality if not detected and treated promptly. Conventional machine learning and deep learning methods have been widely employed for disease classification using leaf imagery, but they often face limitations in terms of computational cost and scalability when dealing with large, high-dimensional datasets. This research paper  presents a Quantum Artificial Intelligence (QAI) based hybrid approach  for the detection and classification of tomato leaf diseases, integrating Quantum Convolutional Neural Networks (QCNNs) for efficient feature extraction and Quantum Support Vector Machines (QSVMs) for high-precision classification. The proposed model is trained and validated on a subset of the Plant Village tomato leaf disease dataset, encompassing major diseases such as early blight, late blight, and leaf mold. Experimental evaluation demonstrates that the QAI-based approach achieves superior accuracy, reduced training time, and enhanced generalization compared to classical AI models. The work underscores the potential of quantum-enhanced models in developing scalable, fast, and reliable disease diagnosis systems for precision agriculture, specifically tailored to high-value crops like tomato.},
        keywords = {Deep Learning, Quantum Artificial Intelligence, QCNN, QSVM.},
        month = {June},
        }