Edge-Ready Citrus Disease Screening Using Classical Vision Features: Computational Efficiency, Robustness, and Threshold-Tunable Canker Detection

Abstract

Abstract Edge-based citrus disease screening requires not only accurate recognition but also low latency, modest memory use, robustness to imperfect image acquisition, and flexible decision thresholds for practical field operation. This study evaluated a lightweight classical vision pipeline as an operational screening engine for orange fruit disease detection under CPU-only deployment constraints. RGB images were represented using HSV color histograms, Local Binary Pattern texture descriptors, and Histogram of Oriented Gradients shape features, followed by one-vs-rest Logistic Regression classification. Beyond classification accuracy, the system was assessed through computational profiling, perturbation robustness, descriptor-level accuracy–latency trade-offs, and threshold-tunable canker detection. The pipeline achieved macro-F1 ≈ 0.94 while requiring only ~ 0.482 ms/image for feature extraction, negligible classification latency, ~ 106 MB RAM, and ~ 6% CPU utilization. Robustness analysis showed stable performance under brightness, contrast, crop, rotation, and moderate noise perturbations. These findings support classical feature-based vision as a practical, transparent, and resource-efficient edge-screening strategy for sustainable citrus disease monitoring.

Plant phenotyping relevance

柑橘果実の病徴・かんきつかんきつ類かいよう病を画像から検出する古典的コンピュータビジョン手法が中心で、精度、頑健性、計算性能、閾値調整を評価しているため。

Code and data availability

The paper evaluates an HSV–LBP–HOG + Logistic Regression citrus disease screening pipeline on an open orange-fruit image dataset. The dataset itself is only described as 'open' and attributed to prior source papers without any authors' public URL in the supplied text. The authors' feature-extraction and training code (

No evidence-backed public reproduction asset is currently recorded.