A Lightweight Edge-AI System for Disease Detection and Three-Level Leaf Spot Severity Assessment in Strawberry Using YOLOv10n and MobileViT-S

Amanova R. Belgibayev B. Mansurova M. Suleimenova M. Amirkhanova G. Tyulepberdinova G.
January 2026 Multidisciplinary Digital Publishing Institute (MDPI)

Computers
2026 #15 Issue 1

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems.

edge-AI , leaf spot , mobile agricultural robot , MobileViT-S , plant disease detection , severity assessment , strawberries , YOLOv10n

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Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Information Systems, International Information Technologies University, Almaty, 050040, Kazakhstan

Department of Big Data and Artificial Intelligence
Department of Information Systems

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