PLANT DISEASE DETECTION

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page Diverse Plant Leaf Disease Detection Using CNN Prof. Rohini Hanchate , Mr. Ninad Dhadphale , Mr Shantanu Dhaybhar , Mr Vedant Pandhare

Abstract: Recent advances in computer vision have led to the development of a robust learning technique that can identify and diagnose plant diseases using photos captured by a camera. This practical approach can help detect various illnesses in different plant species, including apples, corn, grapes, potatoes, tomatoes, and sugar cane. The system's architecture specifically targeted these plants for detection and recognition, and it can detect several plant diseases. To develop deep learning models for plant disease detection and recognition, scientists used 35,000 photos of both disease-free and diseased plant leaves. The system achieved up to 100% accuracy in identifying the type of plant and the diseases affecting it, with the trained model achieving an accuracy rate of 96.5%. The technique involved using convolutional neural networks, computer vision, deep learning, and plant disease recognition.

Keywords: plant disease recognition, deep learning, computer vision, convolutional neural network. Downloads: | DOI: 10.17148/IARJSET.2023.10549 How to Cite: [1] Prof. Rohini Hanchate , Mr. Ninad Dhadphale , Mr Shantanu Dhaybhar , Mr Vedant Pandhare, "Diverse Plant Leaf Disease Detection Using CNN," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2023.10549 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat