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Contrast Enhancement Techniques for Plant Leaf Disease Detection: A Review of CLAHE and Related Preprocessing Methods in CNN-based Diagnosis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 140 |
| Year of Publication: 2026 |
| Authors: R. Senthil, R. Khatwal |
10.5120/ijca88c81a099730
|
R. Senthil, R. Khatwal . Contrast Enhancement Techniques for Plant Leaf Disease Detection: A Review of CLAHE and Related Preprocessing Methods in CNN-based Diagnosis. International Journal of Computer Applications. 187, 140 ( Sep 2026), 57-63. DOI=10.5120/ijca88c81a099730
Abstract
Low and uneven contrast in plant leaf images captured in the field continues to hinder the reliability of convolutional neural network (CNN)-based disease detection systems. This is because lesion boundaries, chlorotic margins, and early necrotic spots often exhibit only minor local intensity differences from healthy tissue. Contrast Limited Adaptive Histogram Equalization (CLAHE) has emerged as a widely adopted preprocessing step to address this issue and has been integrated with various architectures, from custom shallow CNNs to deep transfer-learning backbones such as ResNet50 and VGG16. This paper reviews the application of CLAHE and related contrast-enhancement techniques in the plant disease detection literature. It summarizes the algorithmic foundations of CLAHE and its predecessors, surveys recent studies that incorporate CLAHE into CNN and capsule-network pipelines for leaf disease classification, and compares the datasets, architectures, and reported results across these works. While CLAHE is consistently reported to improve feature visibility and subsequent classification performance, very few studies systematically vary or justify its two governing parameters—clip limit and tile grid size. Even fewer report the statistical significance of the resulting improvements. The review concludes by identifying specific research gaps, including the necessity for controlled parameter sweeps, baseline comparisons against alternative enhancement methods, and standardized statistical validation, all of which could be addressed in future controlled studies.
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