Leaf Disease Detection Using Convolution Neural Network with RESNET50

T. Chandra Sekhar Rao, Yanamala Anitha, Yanamalamanda Harikrishna, Kanaparthi Lakshmi Prasanna, Sudabathula Anand Venkata Sai and Thummaluru Geethika

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 2, 782-792

Abstract: This project presents an advanced leaf disease detection and classification system leveraging Convolutional Neural Networks (CNNs) with the ResNet50 architecture. The system preprocesses input leaf images by standardizing resolution and applying median filtering to remove noise, ensuring optimal image quality for analysis. A fine-tuned ResNet50 model, pre-trained on extensive datasets, is employed to classify leaves as normal or abnormal. For abnormal cases, segmentation techniques are applied to isolate and identify affected regions, improving the precision of disease detection. The model is further trained to classify specific diseases and assess their progression stages. To enhance usability, the system automates email notifications to registered users, providing detailed reports on the detected disease, its stage, and recommended pesticide treatments. By integrating machine learning and image processing techniques, this project delivers a seamless and efficient workflow for real-time diagnostics, enabling proactive disease management and improved agricultural productivity.

Keywords: Leaf Disease Related Dataset; Deep Learning; Convolution Neural Network; Image Processing Techniques; segmentation and accuracy (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST251222622 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST251222622/IJSRST251222622 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i2:id:727

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().