Special Session on
Quantum-Assisted Machine Learning for Intelligent Crop Disease Detection and Smart Agricultural Systems

Aim and Scope

The aim of this work is to explore the potential of quantum-assisted machine learning techniques for intelligent crop disease detection within smart agricultural environments. Crop diseases pose a significant challenge to global agricultural productivity, often leading to substantial economic losses and threats to food security. Therefore, the development of efficient and automated disease detection systems has become an important area of research in modern agriculture.

Recent advancements in artificial intelligence, machine learning, and computer vision have enabled automated methods for identifying plant diseases through image analysis and data-driven approaches. However, the increasing volume and complexity of agricultural datasets require more advanced computational methods to enhance processing efficiency and predictive accuracy.

This study investigates the integration of quantum-assisted computational approaches with machine learning models to improve the performance of crop disease detection systems. By combining concepts from artificial intelligence, quantum computing, and agricultural informatics, the research aims to contribute to the development of intelligent and efficient systems for crop health monitoring and disease diagnosis.

The scope of this research includes the application of image-based disease detection techniques, machine learning algorithms, and emerging quantum-inspired computational frameworks to support precision agriculture and sustainable farming practices. The proposed approach seeks to demonstrate how advanced computing technologies can enhance agricultural decision-making and crop health management.

Topics of Interest

The research addresses the following key areas of interest:

• Quantum-assisted machine learning techniques
• Artificial intelligence applications in agriculture
• Deep learning methods for crop disease detection
• Computer vision and image processing for plant health monitoring
• Image-based plant disease diagnosis systems
• Quantum computing concepts in data analytics
• Hybrid computational models for agricultural applications
• Intelligent decision support systems for crop monitoring
• Emerging computing technologies for sustainable agriculture

Paper Submission

Potential authors may submit their manuscripts for presentation consideration through ICETCI 2026 submission system electronically at https://edas.info/N34670 , following the conference guidelines. All submissions will go through peer review process. To submit your paper to this special session, you have to choose our special session title on the submission page.

Important Dates

Organizers

Deepika Kumari is a PhD scholar in the Department of Data Science at Anurag University. Her research interests are artificial intelligence, machine learning, and emerging computing technologies. Her work focuses on intelligent data analysis and the application of advanced computational techniques for solving real-world problems in domains such as smart agriculture and intelligent systems. She is particularly interested in exploring the integration of machine learning and quantum-inspired computational approaches for developing innovative solutions in crop health monitoring and agricultural analytics.

Email ID: deepikavyas424@gmail.com