International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Crop Disease Detection Using Lightweight Deep Learning Model for Smartphone

Author(s) Ankit Rathod, Dhruv Panchal, Prof. Neha Minocha, Dr. Dulari Bhatt
Country India
Abstract This research presents the design and implementation of a smartphone-based application for the detection of crop diseases using deep learning techniques, aiming to provide farmers with an accessible, real-time solution for plant health monitoring. A customized version of the PlantVillage dataset, which includes additional manually curated and augmented images, was used to train a DenseNet201 convolutional neural network (CNN) on Google Colab. The trained model achieved an impressive 96% accuracy in classifying multiple types of crop diseases. To ensure accurate input validation, a secondary model was developed using Google’s Teachable Machine to distinguish between leaf and non-leaf images, achieving 99% accuracy. Both models were converted into the TensorFlow Lite (.tflite) format to optimize performance on mobile devices. The application was developed using the Flutter framework in Visual Studio Code and deployed on Android, allowing users to capture or upload images for instant disease diagnosis without requiring a constant internet connection. This dual-model approach ensures robustness and improves user confidence by pre-validating input images before disease predictionThe system's exceptional accuracy, usability, and deployment ease make it especially beneficial for smallholder farmers and agricultural extension professionals. In line with the objectives of digital farming and precision agriculture, this approach provides better validity, usefulness, and accessibility than previous studies in the sector.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-29
Cite This Crop Disease Detection Using Lightweight Deep Learning Model for Smartphone - Ankit Rathod, Dhruv Panchal, Prof. Neha Minocha, Dr. Dulari Bhatt - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.4410
DOI https://doi.org/10.71097/IJSAT.v16.i2.4410
Short DOI https://doi.org/g9gx6x
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org