{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:10:57Z","timestamp":1791483057283,"version":"4.3.4"},"reference-count":80,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world\u2019s food source and cover more than 56% of the world\u2019s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method.<\/jats:p>","DOI":"10.3390\/a15030075","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T07:22:35Z","timestamp":1645687355000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Machine Learning in Cereal Crops Disease Detection: A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7533-2187","authenticated-orcid":false,"given":"Fraol Gelana","family":"Waldamichael","sequence":"first","affiliation":[{"name":"Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-2021","authenticated-orcid":false,"given":"Taye Girma","family":"Debelee","sequence":"additional","affiliation":[{"name":"Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia"},{"name":"College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5591-2240","authenticated-orcid":false,"given":"Yehualashet Megersa","family":"Ayano","sequence":"additional","affiliation":[{"name":"Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9764-6427","authenticated-orcid":false,"given":"Samuel Rahimeto","family":"Kebede","sequence":"additional","affiliation":[{"name":"Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia"},{"name":"Department of Electrical and Computer Engineering, Debre Berhan University, Debre Berhan 445, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Debelee, T.G., Kebede, S.R., Schwenker, F., and Shewarega, Z.M. 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