Detection of Plant Infections by Using Image Processing

DOI

10.2991/978-94-6463-948-3_74How to use a DOI?

Keywords

Plant disease detection; Image processing; K-means clustering; GLCM; Machine learning; Precision agriculture; Crop health monitoring

Abstract

The role of agriculture in global food security remains significant, but crop diseases continue to pose major challenges to yield and quality. Early approaches based on manual inspection are slow, costly, and impractical at scale. With the rise of artificial intelligence and image processing, automated plant disease detection has become a practical solution. In this research, we propose a Convolutional Neural Network (CNN)-based classification system that integrates preprocessing, segmentation, augmentation, and classification to detect infections in tomato leaves. A dataset of 1,850 raw images was expanded to 9,250 images (5× augmentation) using resizing, filtering, histogram equalization, background removal, and geometric transformations to simulate real-world variations. After preprocessing, the CNN classifier achieved an accuracy of 98.24%, precision of 98.2%, recall of 98.4%, and F1-score of 98.2%, outperforming classical machine learning models such as Decision Tree, KNN, Random Forest, and SVM. Experimental evaluation confirms that the proposed model is scalable, reliable, and suitable for real-world deployment, contributing to precision agriculture by providing a cost-effective and accurate disease detection framework.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sonali S. Bhalerao
AU  - Sanjay P. Ghanwat
AU  - Ashok R. Tuwar
AU  - Abdul Hannan R. Dalal
PY  - 2026
DA  - 2026/01/06
TI  - Detection of Plant Infections by Using Image Processing
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 1080
EP  - 1098
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_74
DO  - 10.2991/978-94-6463-948-3_74
ID  - Bhalerao2026
ER  -