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@article{206806,
        author = {Mohammed Shaheem and S Chethan and Ashwin Sharma and Moidin Imadh Hasan Moulvi and Ahmad Sinan and Prajwal TR and Kiran P Acharya},
        title = {Machine Learning Based Leaf Detection and Plant Information System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {522-526},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206806},
        abstract = {Accurate identification of medicinal plants holds considerable significance in domains such as healthcare, agriculture, and botanical research. Conventional identification approaches are predominantly reliant on domain expertise and direct visual examination, which renders them inaccessible to general users and susceptible to human error. This paper introduces Leaf Sense, a lightweight intelligent web application that leverages the K-Nearest Neighbors (KNN) machine learning algorithm to classify medicinal plant species from user-submitted leaf photographs. The system currently supports four plant categories Neem, Tulsi, Aloe Vera, and Mango And poise provides predictions along with confidence scores and organized botanical data. It builds on a Python Flask back end paired with an HTML/CSS front-end and features responsive design, error handling, animated transition effects, and dynamic file rendering. An experimental assessment, carried out in a controlled local environment shows that the system reaches an unprecedented classification performance, confirming its viability as an educational and low-scale practical tool. So, the structure can grow easily, reaching more types of plants without trouble. Spotting leaves comes first. Machines learn patterns instead of just counting. One method checks nearby examples to decide. A tool helps name flask-shaped plants too.},
        keywords = {.},
        month = {July},
        }