{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T22:11:24Z","timestamp":1772143884546,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Carl Zeiss Stiftung","award":["P2021-02-009"],"award-info":[{"award-number":["P2021-02-009"]}]},{"name":"German Academic Exchange Service (DAAD)","award":["P2021-02-009"],"award-info":[{"award-number":["P2021-02-009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.<\/jats:p>","DOI":"10.3390\/s23156942","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:28:29Z","timestamp":1691141309000},"page":"6942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop"],"prefix":"10.3390","volume":"23","author":[{"given":"Hirra","family":"Anwar","sequence":"first","affiliation":[{"name":"School of Mechanical and Manufacturing Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan"}]},{"given":"Saad Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad 44000, Pakistan"}]},{"given":"Muhammad Mohsin","family":"Ghaffar","sequence":"additional","affiliation":[{"name":"Microelectronic Systems Design Research Group, University of Kaiserslautern-Landau, 67663 Kaiserslautern, Germany"}]},{"given":"Muhammad","family":"Fayyaz","sequence":"additional","affiliation":[{"name":"Crop Diseases Research Institute, National Agricultural Research Centre, Islamabad 44000, Pakistan"}]},{"given":"Muhammad Jawad","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Manufacturing Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan"},{"name":"Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad 44000, Pakistan"}]},{"given":"Christian","family":"Weis","sequence":"additional","affiliation":[{"name":"Microelectronic Systems Design Research Group, University of Kaiserslautern-Landau, 67663 Kaiserslautern, Germany"}]},{"given":"Norbert","family":"Wehn","sequence":"additional","affiliation":[{"name":"Microelectronic Systems Design Research Group, University of Kaiserslautern-Landau, 67663 Kaiserslautern, Germany"}]},{"given":"Faisal","family":"Shafait","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad 44000, Pakistan"},{"name":"Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad 44000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"617009","DOI":"10.3389\/fsufs.2020.617009","article-title":"Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia","volume":"4","author":"Grote","year":"2021","journal-title":"Front. 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