Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{206685,
        author = {Medha Shetty and Sowmya and B S Rakshita and Nishmita and Ruhaib},
        title = {Arecanut Disease Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {194-198},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206685},
        abstract = {Arecanut (Areca catechu) is one of the significant cash crops grown extensively in India, especially in Karnataka state. But there is significant reduction in productivity due to various diseases like Koleroga, Yellow Leaf Disease, Bud Rot, and Fruit Rot. The conventional method of identifying these diseases includes manual inspections that are labor-intensive and susceptible to mistakes. This study proposes an automated system for arecanut disease detection using deep learning technology. The developed model categorizes input images into three classes—healthy, diseased, and non-arecanut. This work implements a novel architecture called EfficientNetB3 as the base model in transfer learning. The dataset includes 2,485 images, comprising 1,494 for training and 991 for validation, augmented through various techniques.
It has achieved an accuracy score of around 93%, along with high scores for precision, recall, and F1 score for all classes. It has been deployed through Gradio as a web application, which provides the functionality of predicting the classes by uploading the image. Adding the class of “not_arecanut” helps to avoid any erroneous predictions. This method provides a convenient and economical means for diagnosing plant diseases in their early stages. This will enable farmers to manage their crops effectively and minimize crop loss.},
        keywords = {.},
        month = {July},
        }