{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:19:34Z","timestamp":1758845974572,"version":"3.44.0"},"reference-count":14,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":111,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007270","name":"National Institute of Agricultural Sciences","doi-asserted-by":"publisher","award":["PJ01740601"],"award-info":[{"award-number":["PJ01740601"]}],"id":[{"id":"10.13039\/501100007270","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Rural Development Administration, Republic of Korea"},{"DOI":"10.13039\/501100007270","name":"National Institute of Agricultural Sciences","doi-asserted-by":"publisher","award":["PJ01740601"],"award-info":[{"award-number":["PJ01740601"]}],"id":[{"id":"10.13039\/501100007270","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Rural Development Administration, Republic of Korea"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As the importance of precision agriculture grows, scalable and efficient methods for real-time data collection and analysis have become essential. In this study, we developed a system to collect real-time crop images, focusing on physiological disorders in tomatoes. This system systematically collects crop images and related data, with the potential to evolve into a valuable tool for researchers and agricultural practitioners. A total of 58\u2009479 images were produced under stress conditions, including bacterial wilt (BW), Tomato Yellow Leaf Curl Virus (TYLCV), Tomato Spotted Wilt Virus (TSWV), drought, and salinity, across seven tomato varieties. The images include front views at 0 degrees, 120 degrees, 240 degrees, and top views and petiole images. Of these, 43\u2009894 images were suitable for labeling. Based on this, 24\u2009000 images were used for AI model training, and 13\u2009037 images for model testing. By training a deep learning model, we achieved a mean Average Precision (mAP) of 0.46 and a recall rate of 0.60. Additionally, we discussed data augmentation and hyperparameter tuning strategies to improve AI model performance and explored the potential for generalizing the system across various agricultural environments. The database constructed in this study will serve as a crucial resource for the future development of agricultural AI.<\/jats:p>\n               <jats:p>Database URL: https:\/\/crops.phyzen.com\/<\/jats:p>","DOI":"10.1093\/database\/baaf031","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T11:13:10Z","timestamp":1745320390000},"source":"Crossref","is-referenced-by-count":0,"title":["CPDMS: a database system for crop physiological disorder management"],"prefix":"10.1093","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-6953","authenticated-orcid":false,"given":"Jae-Hyeon","family":"Oh","sequence":"first","affiliation":[{"name":"National institute of Agricultural Sciences , Rural Development Administration, 370, Jeonju-si, Jeollabuk-do 54874,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwang-Weon","family":"Jeong","sequence":"additional","affiliation":[{"name":"National 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