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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
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Article Published : 8705
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| Paper Title: | Impact of Image Segmentation Techniques on the Classification of Plant Diseases Using Machine Learning |
| Authors Name: | Dipti Dubey , Dr. Subodhini Gupta |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_202320
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| Published Paper Id: | IJRTI2504113 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Agriculture has always been a vital part of India’s economy, playing a significant role in boosting the country’s GDP. The health and yield of crops are crucial for economic stability, as diseases can severely affect agricultural productivity and resources. That's why it's so important to quickly and accurately identify plant diseases to keep crops healthy. Traditionally, experts have relied on visual inspections to spot these issues, but this method can be quite slow and costly. On the other hand, automated disease detection that focuses on the symptoms visible on plant leaves provides a more efficient, budget-friendly, and precise solution. This study delves into how various image segmentation techniques influence the effectiveness of classical machine learning algorithms in classifying plant diseases. We applied three segmentation methods—K-Means clustering, thresholding, and U-Net deep learning-based semantic segmentation—to isolate areas of interest from images of diseased plants. The segmented images were then analyzed using a pre-trained VGG16 model to extract high-level features, which served as inputs for four classical classifiers: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Logistic Regression. We assessed classification performance using F1-score and accuracy metrics. The results showed that U-Net-based segmentation delivered the best performance, achieving an impressive accuracy of 95% along with consistently strong F1-scores, precision, and recall across all classifiers. In contrast, K-means clustering yielded moderate results, peaking at an accuracy of 69%, while thresholding made significant strides, with SVM reaching an accuracy of 82%. These findings underscore the promise of merging deep learning-based segmentation techniques with traditional machine learning models for effective and efficient plant disease detection, highlighting how these approaches can complement each other in agricultural diagnostics. |
| Keywords: | Image Segmentation algorithms, Plant Disease Classification, Feature Extraction, Classical Machine Learning algorithm, Agricultural Diagnostics. |
| Cite Article: | "Impact of Image Segmentation Techniques on the Classification of Plant Diseases Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 4, page no.b77-b84, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504113.pdf |
| Downloads: | 000506 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504113
Registration ID:202320
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: b77-b84 Country: Jabalpur, Madhya Pradesh, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504113 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504113 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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