{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T23:50:37Z","timestamp":1774655437354,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24992975"],"award-info":[{"award-number":["BR24992975"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2014a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1\u2014mild, S2\u2014moderate, S3\u2014severe) 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 \u2248 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 \u201cYOLOv10n + Ordinal MobileViT-S\u201d 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.<\/jats:p>","DOI":"10.3390\/computers15010063","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T08:08:21Z","timestamp":1768550901000},"page":"63","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lightweight Edge-AI System for Disease Detection and Three-Level Leaf Spot Severity Assessment in Strawberry Using YOLOv10n and MobileViT-S"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8969-582X","authenticated-orcid":false,"given":"Raikhan","family":"Amanova","sequence":"first","affiliation":[{"name":"Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baurzhan","family":"Belgibayev","sequence":"additional","affiliation":[{"name":"Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9680-2758","authenticated-orcid":false,"given":"Madina","family":"Mansurova","sequence":"additional","affiliation":[{"name":"Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8553-5353","authenticated-orcid":false,"given":"Madina","family":"Suleimenova","sequence":"additional","affiliation":[{"name":"Department of Information Systems, International Information Technologies University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3933-5476","authenticated-orcid":false,"given":"Gulshat","family":"Amirkhanova","sequence":"additional","affiliation":[{"name":"Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gulnur","family":"Tyulepberdinova","sequence":"additional","affiliation":[{"name":"Department of Big Data and Artificial Intelligence, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.nut.2011.08.009","article-title":"The strawberry: Composition, nutritional quality, and impact on human health","volume":"28","author":"Giampieri","year":"2012","journal-title":"Nutrition"},{"key":"ref_2","unstructured":"Maas, J.L. 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