{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:16:30Z","timestamp":1783664190019,"version":"3.55.0"},"reference-count":16,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"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>Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.<\/jats:p>","DOI":"10.3389\/fpls.2026.1843877","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:39:24Z","timestamp":1783661964000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SPVD-field: a task-oriented multi-task visual dataset for sweet potato virus disease under real field conditions"],"prefix":"10.3389","volume":"17","author":[{"given":"Ru","family":"Han","sequence":"first","affiliation":[{"name":"College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Shu","sequence":"additional","affiliation":[{"name":"College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University","place":["Nanjing, China"]},{"name":"Guangdong Provincial Key Laboratory for Green Agricultural Production and Intelligent Equipment, School of Computer Science, Guangdong University of Petrochemical Technology","place":["Maoming, China"]},{"name":"School of Engineering and Physical Sciences, University of Lincoln","place":["Lincoln, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grzegorz","family":"Cielniak","sequence":"additional","affiliation":[{"name":"Lincoln Institute for Agri-food Technology, University of Lincoln","place":["Lincoln, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangsu Normal University","place":["Xuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyang","family":"Zang","sequence":"additional","affiliation":[{"name":"College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyuan","family":"Jing","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory for Green Agricultural Production and Intelligent Equipment, School of Computer Science, Guangdong University of Petrochemical Technology","place":["Maoming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihan","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sweetpotato Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Xuzhou Institute of Agricultural Sciences in Jiangsu Xuhuai District","place":["Xuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Academy of Agricultural Sciences","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/tii.2020.2992229","article-title":"Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties","volume":"17","author":"Alencastre-Miranda","year":"2021","journal-title":"IEEE Trans. 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