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Frontiers in Industrial Science
Research Article Published October 9, 2026 Article fis20260011

Confidence-Scheduled Patch Selection for Mobile Crop Disease Recognition

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Abstract

Mobile crop disease recognition is useful for field monitoring, but lightweight models must work under limited computation, unstable lighting, background clutter, and large variation in leaf symptoms. Transformer classifiers can model spatial lesion patterns, yet full patch processing is inefficient when symptoms occupy only a small part of the image. This study proposes confidence-scheduled patch selection for mobile crop disease recognition. The proposed AgriPatch-Scheduler first performs a low-cost scan to estimate lesion probability, color abnormality, and prediction confidence. Patches with clear healthy tissue are compressed early, uncertain lesion patches are sent to deeper transformer layers, and final classification is adjusted by a confidence calibration module to reduce overconfident errors in field images. Data were collected from both public and field sources, including PlantVillage, PlantDoc, and a two-season farm dataset from tomato, maize, grape, potato, and cucumber crops. The final dataset contained 94,300 images across 38 disease and healthy categories, with 31,700 images captured under natural field conditions. AgriPatch-Scheduler achieved 93.6% top-1 accuracy on controlled images and 86.9% on field images. Compared with MobileViT-S, it reduced average multiply-accumulate operations by 29.4% and shortened inference time from 38.2 ms to 24.7 ms on a Snapdragon 8-series mobile processor. For early-stage disease images with lesion areas below 5% of the leaf surface, recall improved from 78.5% to 83.1% because uncertain symptom patches were preserved for deeper analysis. Calibration error decreased from 0.074 to 0.039, making the model more reliable for field decision support. These findings suggest that confidence-scheduled patch selection can balance accuracy, speed, and reliability in mobile agricultural diagnosis.

Keywords
Crop disease recognitionmobile vision modelpatch selectionVision Transformerfield image analysisefficient inferenceconfidence calibration
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Publication details
Journal
Frontiers in Industrial Science
Volume
1 (2026)
Issue
1 · Forthcoming issue
Article number
fis20260011
License
CC BY 4.0