Image Preprocessing and Visual Implementation for Diseased Leaves Based on OpenCV

DOI

10.2991/978-94-6239-737-8_61How to use a DOI?

Keywords

Diseased leaf image preprocessing; OpenCV; CLAHE; Visualization; Smart Agriculture

Abstract

Image preprocessing is a critical prerequisite for computer vision tasks such as leaf disease recognition and target classification, directly affecting the accuracy and efficiency of subsequent models. To address common problems of noise interference, uneven illumination and blurred lesion contours in crop disease leaf recognition scenarios, this paper designs a complete preprocessing pipeline with seven core steps and implements real-time visualization based on Python and OpenCV. The pipeline includes grayscale conversion, Gaussian filtering, CLAHE contrast enhancement, adaptive threshold binarization, morphological closing, Canny edge detection and edge–morphology fusion. Experimental results on real diseased leaf images show that the proposed pipeline effectively removes noise, corrects illumination deviation and strengthens lesion region contours. The signal-to-noise ratio (SNR) is improved by 31.1%, and the target contrast is improved by 49.5%, providing high-quality data support for subsequent leaf disease recognition models such as YOLO.

Copyright

© 2026 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  - Xue Yu
AU  - Yifan Qu
AU  - Hongrui Xu
AU  - Ting Li
PY  - 2026
DA  - 2026/08/18
TI  - Image Preprocessing and Visual Implementation for Diseased Leaves Based on OpenCV
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 508
EP  - 514
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_61
DO  - 10.2991/978-94-6239-737-8_61
ID  - Yu2026
ER  -