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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62082
Volume:
10 |
4 |
April 2026
Pages:
1--
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and Telecommunication Engineering.
TSSM's Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
Electronics and Telecommunication Engineering.
TSSM's Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
Electronics and Telecommunication Engineering.
TSSM's Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
Electronics and Telecommunication Engineering.
TSSM's Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
Agriculture plays a vital role in global economies, yet plant diseases continue to cause significant crop losses every year. Early and accurate identification of plant leaf diseases is essential to prevent these losses and enhance productivity. This paper presents a Plant Leaf Disease Detection System developed using Python, Flask, and deep learning techniques that allows farmers and users to upload an image of a plant leaf and automatically detect the disease. The system leverages a Convolutional Neural Network (CNN) based on the ResNet architecture to analyze leaf patterns, colors, and textures, predicting the disease type along with appropriate treatment recommendations. The web-based interface ensures accessibility without requiring any specialized knowledge. Testing results demonstrate an accuracy of approximately 95% with fast response times of 1–3 seconds per prediction. The system effectively bridges the gap between modern technology and agriculture, promoting smart and sustainable farming practices.
KEYWORDS
The results confirm that the proposed Plant Leaf Disease Detection system significantly outperforms traditional manual methods in terms of speed, accuracy, accessibility, and cost-effectiveness. The ResNet CNN model demonstrates robust performance across diverse leaf image conditions, including varying lighting and backgrounds. The integration of Flask and Bootstrap ensures smooth user interaction, while the crop and fertilizer recommendation modules add practical agricultural value beyond simple disease classification. The system meets all stated objectives and provides a scalable, real-world solution for smart agriculture.
1. Attendance System Using NFC Technology with Embedded Camera on Mobile Device (Bhise, Khichi, Korde, Lokare, 2015) 2. K. Senthamil Selvi, P. Chitrakala, A. Antony Jenitha, “Face Recognition Based Attendance Marking System,” IJCSMC, Vol. 3, Issue 2, February 2014 3. OpenCV Documentation – https://opencv.org/ 4. NumPy – https://numpy.org/ 5. International Journal of Engineering Research & Technology (IJERT) – https://www.ijert.org/research/face-recognition-based-attendance-system-IJERTV9IS060615.pdf 6. GitHub Link – https://github.com/Patelrahul4884/Attendance-Management-system-using-face-recognition
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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