Field-Reliability Analysis of Lightweight Orange Disease Screening: Image-Quality Effects, Failure Taxonomy, and Practical Triage for Edge Deployment

Abstract

Abstract Reliable edge-based citrus disease screening requires more than high benchmark accuracy; it must identify when an image and its prediction are trustworthy under field conditions. This study analyzed a balanced three-class orange image corpus comprising healthy fruit, citrus canker, and melanose using a lightweight HSV–LBP–HOG representation and calibrated confidence profiling. Rather than treating all classifications as equally actionable, the analysis linked prediction confidence with image-quality limitations and residual error patterns. The results showed that difficult cases were concentrated around the early canker–healthy boundary, where weak chromatic changes, subtle rind texture, shadowing, blur, and non-disease surface defects reduced diagnostic reliability. A quality-aware triage scheme was therefore established to separate direct acceptance, image recapture, and expert or local refinement. The findings support a practical field-reliability framework for low-cost orange disease screening on mobile and edge devices.

Plant phenotyping relevance

柑橘病害の画像分類について、画像品質、失敗分類、信頼度校正、現場トリアージを中心に評価しており、植物の病徴・病害状態を推定する手法の検証が主題である。

Code and data availability

The paper uses an open orange-fruit image dataset but provides no public URL or repository identifier for it, and the authors' feature/training code is only offered 'for release upon acceptance,' so no directly actionable public asset exists in the supplied text.

No evidence-backed public reproduction asset is currently recorded.