Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  LeafLense: Advanced Leaf Disease Detection
PaperId: :  26338
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26338
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mayuri Balaso MhavaleD.Y.Patil Technical Campus, Talsande
Gayatri Sampat ChavanD.Y.Patil Technical Campus, Talsande
Sanika Vijaykumar ChandobaD.Y.Patil Technical Campus, Talsande
Ankita Mahesh PatilD.Y.Patil Technical Campus, Talsande
Prof B. S. JadhavD.Y.Patil Technical Campus, Talsande

Abstract

Computer Science & Engineering
Plant Disease Detection, Smart Farming, and Crop Health Monitoring, Deep learning, machine learning, leaf disease detection etc
Plant diseases are a critical issue in agriculture, affecting crop health, reducing yield, and threatening global food security. Early and accurate detection of these diseases is essential to prevent their spread and ensure timely treatment. This project presents a deep learning-based approach to detect plant leaf diseases in crops such as potato, bell pepper, corn, beans, and sunflower. A Convolutional Neural Network (CNN) model is trained on a diverse dataset of leaf images to identify and classify diseases with high accuracy. The system eliminates the need for manual inspection, minimizes human error, and offers a faster, more efficient way to support farmers and agricultural experts in disease diagnosis. To make this solution accessible and user-friendly, a web-based application has been developed using Python and the Flask framework. The frontend is built using HTML, CSS, and JavaScript to provide a smooth, responsive user experience, while SQL is used for data storage and management. Users can upload leaf images through the interface, and the system processes the input using the trained model to predict the disease type along with a confidence score. The project ensures security, scalability, and real-time functionality. With minimal hardware requirements, the system is affordable and easy to implement. It can also be extended to mobile platforms or integrated into smart farming systems for field use. This project highlights the effective use of deep learning and modern web technologies to contribute to precision agriculture, helping improve crop health monitoring and promoting sustainable farming practices.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. "LeafLense: Advanced Leaf Disease Detection" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 2811-2815
MLA Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. "LeafLense: Advanced Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 2811-2815.
APA Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, & Prof B. S. Jadhav. (2025). LeafLense: Advanced Leaf Disease Detection. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 2811-2815.
Chicago Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. "LeafLense: Advanced Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 2811-2815.
Oxford Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. 'LeafLense: Advanced Leaf Disease Detection', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 2811-2815. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/LeafLense__Advanced_Leaf_Disease_Detection_ijariie26338.pdf (Accessed : ).
Harvard Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. (2025) 'LeafLense: Advanced Leaf Disease Detection', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 2811-2815IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/LeafLense__Advanced_Leaf_Disease_Detection_ijariie26338.pdf (Accessed : )
IEEE Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav, "LeafLense: Advanced Leaf Disease Detection," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 2811-2815, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/LeafLense__Advanced_Leaf_Disease_Detection_ijariie26338.pdf [Accessed : ].
Turabian Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. "LeafLense: Advanced Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver Mayuri Balaso Mhavale, Gayatri Sampat Chavan, Sanika Vijaykumar Chandoba, Ankita Mahesh Patil, and Prof B. S. Jadhav. LeafLense: Advanced Leaf Disease Detection. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 2811-2815. Available from: https://ijariie.com/AdminUploadPdf/LeafLense__Advanced_Leaf_Disease_Detection_ijariie26338.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Real-Time Heart Disease Prediction Using Machine LearningCOMPUTER SCIENCE AND ENGINEERINGP. Sri Pavan Hari Charan Download
HAND AND FACE LANDMARKS DETECTION USING MEDIA PIPE AND AICOMPUTER SCIENCE AND ENGINEERINGK. V. Bharath Kumar Download
VEHICLES SPEED DETECTION SYSTEM USING OPENCVCOMPUTER SCIENCE AND ENGINEERINGS. Santosh Download
MACHINE LEARNING FOR REAL-TIME HEART DISEASE PREDICTIONCOMPUTER SCIENCE AND ENGINEERINGM. Lakshmi kanth Download
DETECTION OF THYROID DISORDERS USING MACHINE LEARNING APPOARCHCOMPUTER SCIENCE AND ENGINEERINGP. Shamiulla Download
Events Recommendation SystemComputer Science and EngineeringDharsana S Download
Online Attendance Using Web ApplicationComputer EngineeringMrs. P. Saranya Download
Automated Pneumonia Detection Using ResNet50 Deep Learning ModelDepartment of Computer Science & Engineering, D.Y.Patil Technical Campus, Shivaji University, KolhapurSohan Sanjay Kamalakar Download
MULTI-DISEASE DETECTION USING MACHINE LEARNINGComputer EngineeringNamithadevi N N Download
NETWORK TRAFFIC ANALYSIS WITH BIG DATA AND DEEP LEARNING TECHNIQUESCOMPUTER SCIENCE AND ENGINEERINGD VYSHNAVI Download
Diabetic Retinopathy Detection Using Deep LearningComputer Science EngineeringSandesh Madhukar Shetake Download
Instagram Automation toolComputer EngineeringGaikwad Samrat Download
SURVEY ON CONTENT-BASED IMAGE RETRIEVAL (CBIR) SYSTEMComputer EngineeringJAGATHKRISHNA TS Download
PadosiMart: A Hyperlocal Digital Platform for Empowering Local CommerceComputer EngineeringSwaroop Shashikant Jadhav Download
LeafLense: Advanced Leaf Disease DetectionComputer Science & EngineeringMayuri Balaso Mhavale Download
12