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@article{199706,
        author = {GUDIPALLY AISHWARYA and Terala Soumya and Barigela Sneha and Bashpangu Meghana},
        title = {AI-Driven Leaf Disease Detection Using Computer Vision for Enhancing Computational Thinking Skills},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15450-15457},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199706},
        abstract = {Today, Artificial Intelligence (AI) technologies receive widespread adoption yet students in their beginning educational years encounter limited opportunities to work with these systems. The research demonstrates a basic leaf disease detection system which uses computer vision technology to enhance users' computational thinking abilities. The system performs plant leaf image analysis through initial processes which include resizing and removing unwanted elements. 
The researchers employ a Convolutional Neural Network (CNN) to determine whether the leaf exists in a healthy state or a diseased condition. The project demonstrates to students how real-world problems receive solutions through the application of AI techniques. The program enables users to develop skills in logical reasoning and pattern identification and both methods of solving problems and sequential thinking. The system demonstrates its ability to accurately identify plant diseases while serving as an effective tool for conducting basic plant health assessments. The research links academic knowledge to real-world usage while providing students with an accessible method to grasp artificial intelligence principles.},
        keywords = {Artificial Intelligence, Computer Vision, Leaf Disease Detection, CNN, Computational Thinking.},
        month = {April},
        }