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Published Paper Details:
Plant Leaf Disease Detection Using Machine Learning Algorithms
Authors
Pratik Machindranath Gaikwad,Abhijit Ashok Hipparkar,Prajwal Bhikaji Kanawade,Vitthal Malhari Dhade,Kiran Ramchandra Ghodake
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Keywords
CNN, Deep Learning, ResNet, Transfer Learning, Classification, Plant Disease Detection
Abstract
This paper explores the efficacy of utilizing a pre-trained ResNet34 model for agricultural disease identification via image recognition. With a focus on a prevalent vegetable, the study seeks to raise farmers' awareness of innovative technologies to combat plant diseases. Employing a machine learning and image processing approach, an accurate algorithm is proposed for detecting leaf diseases in vegetables and plants. The investigation centers on samples of plant leaves exhibiting disorders, aiming to assist farmers in early disease detection. Leveraging the widespread availability of smartphones and advancements in computer vision, particularly convolutional neural networks (CNNs), the research aims to address the persistent threat of plant diseases to smallholder farmers' livelihoods and food security.
The developed model demonstrates the ability to distinguish between healthy leaf tissue and seven distinct plant illnesses. Deployed as a user-friendly web application, the model ensures accessibility for farmers. Training and validation utilize a meticulously curated dataset of leaf images captured under controlled conditions. The proposed methodology achieves impressive results, with an accuracy rate of 97.2% and an F1 score exceeding 96.5% based on rigorous validation procedures.
Amid rapid population growth, agriculture plays a critical role in sustaining human life. Early disease prediction is essential for food security, yet remains challenging. This paper aims to equip farmers with knowledge of cutting-edge technologies to mitigate plant diseases, particularly focusing on a widely available vegetable. Through the proposed machine learning and image processing approach, coupled with advanced algorithms, farmers can enhance disease detection in vegetables and plants, thereby safeguarding agricultural yields and ensuring food security.
IJCRT's Publication Details
Unique Identification Number - IJCRT24A5812
Paper ID - 262705
Author type - Indian Author
Page Number(s) - q36-q44
Pubished in - Volume 12 | Issue 5 | May 2024
DOI (Digital Object Identifier) -
No Of Downloads - 18
Author Country - India, 415523, Satara, Satara, 415523, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Pratik Machindranath Gaikwad,Abhijit Ashok Hipparkar,Prajwal Bhikaji Kanawade,Vitthal Malhari Dhade,Kiran Ramchandra Ghodake, "Plant Leaf Disease Detection Using Machine Learning Algorithms", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.q36-q44, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A5812.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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