{"id":"AIBD-1199","title":"Gyeonggi-do Agricultural Research and Extension Services reports that the smart pest and disease diagnosis app reached 90% on farm photos in 2024","summary":"Gyeonggi-do's agricultural research service reports that the deep-learning pest and disease diagnosis app reached a diagnosis rate of 90% or more on leek and cucumber farms in May and October 2024.","date_observed":"2024-10-01","date_basis":"event","date_precision":"month","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"The Rural Development Administration runs the National Crop Pest Management System (NCPMS). Gyeonggi-do Agricultural Research and Extension Services collected images for its deep-learning diagnosis of leek and cucumber diseases from 2021 to 2024. Staff photographed symptoms on farms in Anseong, Pyeongtaek and Hwaseong with smartphones, confirmed the pathogens by culture and DNA methods and labelled the images in the crop pest management program. The dataset held 12,236 images for three leek diseases and six cucumber diseases, including standard images by disease stage. To check the dataset's accuracy, the institute ran field demonstrations in May and October 2024 in which farmers photographed disease symptoms with their own smartphones. The smart pest and disease diagnosis app, built with a deep-learning algorithm, analysed the photos. The institute's 2024 research report, published in June 2025, states that the diagnosis rate was 90% or more in both rounds. It adds that the app can diagnose quickly and accurately on farms. The institute supplied the training data. The report gives no counts of farms or photos behind the rate, and no outcome for the farms is reported.","category":"aggregate result","unexpected":false,"purpose":"The Rural Development Administration's crop pest management program and the provincial institute built the deep-learning dataset so that the smart pest and disease diagnosis app can diagnose leek and cucumber diseases from farmers' photos without an expert.","system":"스마트 병해충 진단 앱 (smart pest and disease diagnosis app, deep-learning image diagnosis on the National Crop Pest Management System, NCPMS)","model_version":null,"developer":"Rural Development Administration (National Crop Pest Management System, as cited in the report), with Gyeonggi-do Agricultural Research and Extension Services supplying the labelled images","deployer":"Leek and cucumber growers in farm demonstrations run by Gyeonggi-do Agricultural Research and Extension Services","location":"Gyeonggi-do, South Korea (image collection at farms in Anseong, Pyeongtaek and Hwaseong)","domain":"Agriculture / crop disease diagnosis","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.1.b.1"],"taxonomy_note":"The report measures a diagnosis rate on farm photos, and the aim is a quick diagnosis on the farm without an expert.","registry_ids":{},"ratings":[{"group":"personal","depth":null,"reason":"The demonstration measured the app's diagnosis rate on farm photos, and the report gives no outcome for the farms."}],"ai_role":{"status":"unassessed","statement":"The institute checked the app's diagnoses of farmers' photos against its own identifications. The report gives no comparison with diagnosis without the app and no counts behind the 90% figure, and the institute supplied the training images."},"evidence":[{"kind":"report","citation":"경기도농업기술원 환경농업연구과 (Gyeonggi-do Agricultural Research and Extension Services, Environmental Agriculture Research Division) (2025). 파, 오이 주요 병해 딥러닝 영상정보 구축 (Building deep-learning image data on major diseases of leek and cucumber). 2024년도 시험연구보고서 (2024 Research Report), pp. 372-380, June 2025.","title":"2024년도 시험연구보고서: 파, 오이 주요 병해 딥러닝 영상정보 구축","url":"https://nongup.gg.go.kr/wp-content/uploads/sites/2/2025/06/report_24_envi_03.pdf","date":"2025-06-01","written_by":"evaluator","quote":"실증을 통해 학습된 딥러닝 데이터세트의 정확성과 재현성을 검토하였고, 1차, 2차 모두 90% 이상의 진단율을 확인하였다"},{"kind":"report","citation":"경기도농업기술원 환경농업연구과 (Gyeonggi-do Agricultural Research and Extension Services, Environmental Agriculture Research Division) (2025). 파, 오이 주요 병해 딥러닝 영상정보 구축 (Building deep-learning image data on major diseases of leek and cucumber). 2024년도 시험연구보고서 (2024 Research Report), pp. 372-380, June 2025. English abstract.","title":"2024년도 시험연구보고서: 파, 오이 주요 병해 딥러닝 영상정보 구축","url":"https://nongup.gg.go.kr/wp-content/uploads/sites/2/2025/06/report_24_envi_03.pdf","date":"2025-06-01","written_by":"evaluator","quote":"The trials demonstrated that the developed app exhibited high accuracy and reproducibility, enabling fast and accurate disease diagnosis on farms, and is expected to make a significant contribution to pest and disease management."}],"replication":{"produced_by":["Staff photographed disease symptoms on leek and cucumber farms from 2021 to 2024, confirmed the pathogens and labelled 12,236 images for deep learning.","Farmers photographed symptoms with their own smartphones in two demonstration rounds, in May and October 2024.","The app analysed the photos with a deep-learning algorithm."],"must_hold":["The disease must be one of the three leek or six cucumber diseases in the training data.","Photos must show symptoms as they appear on farms, since the dataset was built by disease stage and plant part."],"echoes":[]},"paired_incident":null,"headline":"*스마트 병해충 진단 앱* tops 90% on leek and cucumber farm photos","metric":"diagnosis rate of 90% or more","comparator":null,"later":null,"learns_from_data":{"value":"shown","note":"The report says that the app analysed the photos with a deep-learning algorithm trained on the labelled dataset.","quote":"촬영된 영상데이터는 딥러닝 알고리즘을 적용한 진단 애플리케이션으로 분석하였고, 이 과정에서 병해충 진단 정확도 및 현장 적용상의 개선 사항을 평가하였다.","url":"https://nongup.gg.go.kr/wp-content/uploads/sites/2/2025/06/report_24_envi_03.pdf"},"benefit_dimensions":["functional","economic"],"benefit_categories":["environmental_impact"],"scsp_example":"Environmental impacts","size":{"reach":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The field demonstrations tested the app's diagnosis rate, and the report counts no farm that gained."},"duration":{"band":null,"fact":null,"quote":null,"url":null,"reason":"No gain for any farm is documented, so nothing lasting can be measured."},"distribution":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The report names no groups of growers and documents no gain for any."},"frequency":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The report describes two demonstration rounds and says nothing about how often growers use the app."}},"value":null,"developer_country":"South Korea","deployer_country":"South Korea","unrated_because":"The report measures a diagnosis rate in two demonstration rounds and reports no realized outcome for any grower.","brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the report of Gyeonggi-do Agricultural Research and Extension Services.","source":"https://nongup.gg.go.kr/wp-content/uploads/sites/2/2025/06/report_24_envi_03.pdf","editor":"AIBD"},{"date":"2026-11-01","change":"Placed the benefit under its broad categories and recorded its reach, duration, distribution and frequency from its sources.","source":null,"editor":"AIBD"}],"confidence":"claimed","shade":"low","claim_status":"The evaluator's account","claim_basis":"Sources: the evaluators' report. 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