{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T11:57:03Z","timestamp":1790942223908,"version":"4.1.0"},"reference-count":14,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    Field-deployable plant-health vision systems require datasets that preserve natural variation in illumination, scale, occlusion, and background clutter. This Data Report describes a fixed public release of 500 RGB JPEG images of cucumber leaves collected at three protected-cultivation facilities in Kazakhstan. The collection contains 250 healthy-leaf images and 250 affected-leaf images. Decoder-level inspection confirmed three native image dimensions: 3840 \u00d7 2160 pixels (387 images), 4608 \u00d7 3456 (112), and 4000 \u00d7 2252 (one). The affected subset is paired with 250 text files containing 2,705 rectangular annotations in normalized YOLO center-coordinate format: 1,741 class-0 boxes identify visible non-green color and 964 class-1 boxes identify visible tissue damage. Class 1 includes visible tissue deterioration regardless of whether the apparent cause is disease-associated, mechanical, pest-related, senescent, or otherwise uncertain. The two phenotype labels are operationally non-exclusive: 215 affected images contain both classes, and 116 contain at least one positive-area cross-class box overlap. These labels describe observable phenotypes and do not provide pathogen, disease-species, cultivar, or causal diagnoses. The release supplies bounding boxes, not pixel-level masks; it therefore supports image classification, object detection, and rectangular region localization, but not conventional supervised segmentation. A complete decoder, structure, coordinate, exact-hash, filename, and coarse perceptual-hash audit confirmed one-to-one affected image-label pairing and valid five-field rows, while identifying 42 boundary-crossing boxes in 36 files, 19 byte-identical pairs, and three additional high-similarity candidate pairs. The intentionally balanced final folders are a curated sample and must not be interpreted as natural prevalence. The dataset is publicly available in Mendeley Data, version 1, under CC BY 4.0 at\n                    <jats:ext-link>https:\/\/doi.org\/10.17632\/mb3vnhpk8z.1<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1962647","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T11:36:38Z","timestamp":1790940998000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An annotated cucumber leaf image dataset for detection of visible damage and abnormal color symptoms in complex cultivation environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Batyrkhan","family":"Omarov","sequence":"first","affiliation":[{"name":"Joldasbekov Institute of Mechanics and Engineering","place":["Almaty, Kazakhstan"]},{"name":"International Information Technology University","place":["Almaty, Kazakhstan"]},{"name":"Narxoz University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayat","family":"Ibrayev","sequence":"additional","affiliation":[{"name":"Joldasbekov Institute of Mechanics and Engineering","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurbibi","family":"Imanbayeva","sequence":"additional","affiliation":[{"name":"Joldasbekov Institute of Mechanics and Engineering","place":["Almaty, Kazakhstan"]},{"name":"Satbayev University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. 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Agric."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1962647\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T11:36:39Z","timestamp":1790940999000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1962647\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,2]]},"references-count":14,"alternative-id":["10.3389\/fpls.2026.1962647"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1962647","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,2]]},"article-number":"1962647"}}