{"id":"AIBD-1198","title":"Kyoto and Mie prefectural institutes report that the MAFF project's pest and disease diagnosis AI is 80% or more accurate on new images","summary":"Kyoto's agricultural technology center reports that the pest and disease diagnosis AI reached 80% or more accuracy on 4,976 new eggplant images. Mie's institute reports recall of roughly 80% or more.","date_observed":"2022-01-01","date_basis":"source publication","date_precision":"year","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Japan's Ministry of Agriculture, Forestry and Fisheries commissioned a project (JP17935051) in which NARO and prefectural institutes built a pest and disease diagnosis AI. It reads smartphone photos of tomato, cucumber, strawberry and eggplant. It was developed from more than 700,000 damage images collected by experts. Nihon Nohyaku built it into its free app レイミーのAI病害虫雑草診断 on 1 March 2022. The Kyoto Prefectural Agriculture, Forestry and Fisheries Technology Center collected 19,366 disease and 12,375 pest images of eggplant for training. It then had the AI diagnose newly photographed images, 2,613 of diseases and 2,363 of pests, and reports that accuracy reached 80% or more. A trial survey of 62 extension advisers and growers found expectations for the system alongside requests for higher accuracy. The Mie Prefecture Agricultural Research Institute, which also supplied training images, reports recall of roughly 80% or more and lists new farmers and trainees as the intended users. Both institutes took part in the project. 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Neither compares growers' diagnoses or crop outcomes with and without the app, and both institutes supplied training images as project participants."},"evidence":[{"kind":"report","citation":"京都府農林水産技術センター 生物資源研究センター 応用研究部 (Kyoto Prefectural Agriculture, Forestry and Fisheries Technology Center, Biological Resources Research Center, Applied Research Division) (2022). ＡＩを活用した画像によるナス病害虫診断技術の開発 (Development of an AI image-based eggplant pest and disease diagnosis technology).","title":"ＡＩを活用した画像によるナス病害虫診断技術の開発","url":"https://www.pref.kyoto.jp/shigenken/documents/aisindann.pdf","date":"2022-01-01","written_by":"evaluator","quote":"AI の精度検証は、新たに撮影した病害虫被害画像を AI に診断させ、病害 2,613 件、虫害 2,363 件について実施し、最終的には 80%以上の精度を確保することができた。"},{"kind":"report","citation":"三重県農業研究所 (Mie Prefecture Agricultural Research Institute) (2022). 令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI (FY2021 results: a pest and disease diagnosis AI that identifies pests and diseases from smartphone camera images).","title":"令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI","url":"https://www.pref.mie.lg.jp/common/content/001037004.pdf","date":"2022-01-01","written_by":"evaluator","quote":"病害虫診断AIによる診断精度は、概ね 80%以上の再現率です。"}],"replication":{"produced_by":["The AI was developed from more than 700,000 pest and disease damage images collected by experts at agricultural research institutes.","Kyoto's center photographed 19,366 disease and 12,375 pest images of eggplant by plant part, severity and time for training.","The accuracy check used newly photographed images that were not part of training."],"must_hold":["The crop and the pest or disease must be among those the AI was trained on, ten for eggplant.","The photo must be taken with a smartphone camera in the field, as in the checks."],"echoes":[]},"paired_incident":null,"headline":"Prefectures find *病害虫診断AI* 80% accurate on new pest images","metric":"80% or more accuracy on 4,976 new eggplant images","comparator":null,"later":null,"learns_from_data":{"value":"shown","note":"Mie's sheet says that the AI was developed from more than 700,000 pest and disease damage images collected by experts.","quote":"病害虫診断 AI は、農業研究所などの専門家が収集した 70 万 枚以上の病害虫被害画像をもとに開発されています。","url":"https://www.pref.mie.lg.jp/common/content/001037004.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 reports test the AI on new photos and survey 62 advisers and growers, and count no grower whose crop gained."},"duration":{"band":null,"fact":null,"quote":null,"url":null,"reason":"No gain for any grower is documented, so nothing lasting can be measured."},"distribution":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The reports name new farmers and trainees as intended users but show no gain for any group."},"frequency":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The reports describe accuracy tests and say nothing about how often growers use the app."}},"value":null,"developer_country":"Japan","deployer_country":"Japan","unrated_because":"Both reports measure diagnostic accuracy on photographs and survey opinions, so no realized outcome for growers is reported.","brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the report of the Kyoto Prefectural Agriculture, Forestry and Fisheries Technology Center, with the result sheet of the Mie Prefecture Agricultural Research Institute.","source":"https://www.pref.kyoto.jp/shigenken/documents/aisindann.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: two reports from the evaluators. 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Center, Applied Research Division) (2022). ＡＩを活用した画像によるナス病害虫診断技術の開発 (Development of an AI image-based eggplant pest and disease diagnosis technology).","title":"ＡＩを活用した画像によるナス病害虫診断技術の開発","url":"https://www.pref.kyoto.jp/shigenken/documents/aisindann.pdf","date":"2022-01-01","written_by":"evaluator","quote":"AI の精度検証は、新たに撮影した病害虫被害画像を AI に診断させ、病害 2,613 件、虫害 2,363 件について実施し、最終的には 80%以上の精度を確保することができた。"},{"kind":"report","citation":"三重県農業研究所 (Mie Prefecture Agricultural Research Institute) (2022). 令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI (FY2021 results: a pest and disease diagnosis AI that identifies pests and diseases from smartphone camera images).","title":"令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI","url":"https://www.pref.mie.lg.jp/common/content/001037004.pdf","date":"2022-01-01","written_by":"evaluator","quote":"病害虫診断AIによる診断精度は、概ね 80%以上の再現率です。"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].id":{"state":"value_present","value":{"kind":"identifiers","identifiers":["AIBD-1198-source-0001","AIBD-1198-source-0002"],"role":"Existing evidence items, in their original order"},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"sources[].language":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"report","citation":"京都府農林水産技術センター 生物資源研究センター 応用研究部 (Kyoto Prefectural Agriculture, Forestry and Fisheries Technology Center, Biological Resources Research Center, Applied Research Division) (2022). ＡＩを活用した画像によるナス病害虫診断技術の開発 (Development of an AI image-based eggplant pest and disease diagnosis technology).","title":"ＡＩを活用した画像によるナス病害虫診断技術の開発","url":"https://www.pref.kyoto.jp/shigenken/documents/aisindann.pdf","date":"2022-01-01","written_by":"evaluator","quote":"AI の精度検証は、新たに撮影した病害虫被害画像を AI に診断させ、病害 2,613 件、虫害 2,363 件について実施し、最終的には 80%以上の精度を確保することができた。"},{"kind":"report","citation":"三重県農業研究所 (Mie Prefecture Agricultural Research Institute) (2022). 令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI (FY2021 results: a pest and disease diagnosis AI that identifies pests and diseases from smartphone camera images).","title":"令和３年度 三重農研成果情報: スマートフォンカメラ撮影画像から病害虫を識別できる病害虫診断AI","url":"https://www.pref.mie.lg.jp/common/content/001037004.pdf","date":"2022-01-01","written_by":"evaluator","quote":"病害虫診断AIによる診断精度は、概ね 80%以上の再現率です。"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"Kyoto's agricultural technology center reports that the pest and disease diagnosis AI reached 80% or more accuracy on 4,976 new eggplant images. 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