DETECTING PLANT SPECIES HEALTH USING ARTIFICIAL INTELLIGENCE

Registration ID: IJNRD_215956

Published ID: IJNRD2403385

DOI: Click Here to Get

Mr. RAMBABU ATMAKURI , Ms. BOMMAREDDY VENKATA SANTHOSHI , Mr. AGALDIVITY SUMITH , Ms. KOTHWAL AISHWARYA , Ms. YERRA GOWRI GAYATHRI

Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

A lot of study in computer vision and agriculture is done on using Convolutional Neural Networks (CNN) and the VGG16 architecture to find diseases in rice plants. One of the main goals is to make a model that can correctly tell the difference between pictures of different rice plant diseases. CNN is a deep learning method that is often used to recognize pictures. VGG16 is a type of CNN that is known for how well it does at classifying images. To train the model, we need a set of pictures of both healthy and sick rice plants, with enough of each type of disease shown. The computer can then learn to tell the difference between the diseases. We use the VGG16 architecture to teach the CNN model once we have the information. The model learns to find patterns in pictures that show whether a plant is healthy or sick during training. We can use a different set of pictures to test the model and see how well it does after training. We use precision, memory, and the F1 score to measure how accurate it is. This study could help farmers a lot by finding and treating diseases in rice plants early on. This could lead to higher crop output and more food security. One interesting way to use technology to help farmers is in this way. Deep Convolutional Neural Networks (DCNN) and transfer learning are used in the suggested method, which seems to be good at correctly identifying six types of diseases in rice plants. Farmers can keep an eye on their fields more efficiently by using IoT and drone technology. This cuts down on the need for costly manual checks. The proposed method seems like it could be used in the real world because it is very accurate. It's interesting that this method works better than similar ones that have been used in previous studies. Overall, using this method could increase crop output and help ensure food security by making it easier to find and treat rice plant diseases early on.

Published Paper   E-Certificate

Mr. RAMBABU ATMAKURI, Ms. BOMMAREDDY VENKATA SANTHOSHI, Mr. AGALDIVITY SUMITH, Ms. KOTHWAL AISHWARYA, & Ms. YERRA GOWRI GAYATHRI (March-2024). DETECTING PLANT SPECIES HEALTH USING ARTIFICIAL INTELLIGENCE. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), d638-d645. https://ijnrd.org/papers/IJNRD2403385.pdf

Volume 9 Issue 3, March-2024

Pages : d638-d645

Paper Reg. ID: IJNRD_215956

Published Paper Id: IJNRD2403385

Downloads: 000121983

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403385.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403385

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details