벌통 내외부 환경 정보 및 벌 생존 연관 정보를 다루는 스마트 벌통 센서 데이터, 스마트벌통 꿀벌 출입구 영상 데이터, 스마트벌통 꿀벌 움직임 학습용 데이터로 구성되며 국립농업과학원의 첨단온실 내 벌통 4개를 대상으로 수집한 데이터를 정제/가공을 거쳐 제공합니다.

지능형 양봉 데이터’는 꿀벌의 생애단계별(알, 애벌레, 번데기, 수일벌, 여왕벌), 종봉별(이탈리안, 카니올란, 한봉, 호박벌) 데이터와 생애이슈인 백묵병 데이터 총 12개 클래스로 이루어진 이미지 데이터

시군별 계획대비 수확벌채, 수종갱신 벌채, 피해목 벌채 등 세부 벌채유형별 자원순환형 벌채 추진 현황 자료를 조회할 수 있습니다

곤충 스마트팜에 적용할 수 있는 누에 4종(백견, 황견, 나용, 삼면잠)과 쌍별귀뚜라미의 RGB 이미지 데이터와 열화상 이미지 데이터로, 알부터 성충까지 생애 주기를 일정 시간 간격으로 촬영하여 AI가 학습할 수 있는 형태로 가공함

국립농업과학원의 스마트팜 첨단 온실에서 수집되는 데이터, 스마트팜 운영을 위한 온실 환경 모니터링 및 작물 생장 분석 데이터 셋으로, 벌 활동 추적, 환경 센서 측정값, 작물 생장 영상 및 AI 학습용 라벨링 데이터 등을 포함합니다.

,In 2014–2022, USDA-ARS Tucson, AZ, by itself and in collaboration with other precision apiculture (PA) research programs, including the PA program at Utah State University, and several commercial operations, acquired a large reservoir of multi-sensor data, including thousands of frame photographs and sensor measurements, from field experiments with managed honey bee colonies. This reservoir is a loose collection of hive frame photos, CSV files, spreadsheets, and hive inspection text logs. Our project explores and exploits this reservoir and makes public its curated subsets. This dataset is the first such subset we curated in 2024-25 under USDA-NIFA Award 205732 "DSFAS - Exploration and Exploitation of the 2014-2022 USDA-ARS Tucson, AZ Digital Data Reservoir of Field Experiments with Managed Honey Bee Colonies.",The zipped directory ANNOTATED_HIVE_FRAMES includes 13 image subdirectories with annotated images.,1) 2013_07_28_CHBRC -- 57 Files,2) 2014_07_30_12_CHBRC -- 111 Files,3) 2015_02_11_MAC_RR -- 660 Files,4) 2016_03_30_HOOPS -- 153 Files,5) 2017_02_01_SRER_BEAR_CAGE -- 87 Files,6) 2018_02_13_SRER_SC_complete_3_9_25 -- 195 Files,7) 2018_04_18_SRER_SC_Methoxy -- 366 Files,8) 2019_07_11_SRER_BC_Neonic -- 60 Files,9) 2020_02_27_RR_Hive_Directions -- 36 Files,10) 2021_06_08_CHBRC_VLAD -- 282 Files,11) 2021_09_27_RR_ColdStor -- 855 Files,12) 2021_02_11_CT_ColdStor -- 111 Files,13) 2014_12_15_50_CHBRC --- 30 files,The name of each subfolder includes a year, a month, and a date on which the frame photos were taken, followed by the location of the apiary where the photos were taken. The de-abbreviations are as follows:,CHBRC -- Carl Hayden Bee Research Center,MAC -- Maricopa Agriculture Center,RR -- Red Rock Agriculture Center,HOOPS -- one of the apiaries at CHBRC,SRER -- Santa Rita Experimental Range,SRER -- Shipping Corrals,CT -- Cow Town,Each of the 13 subdirectories has three subsubdirectories: PNG/, XML/, TXT/.,PNG/ -- hive frame photos in PNG format;,XML/ -- XML annotations of images in PNG/ with LabelImg,TXT/ -- TXT annotations of images in PNG/ for YOLO training,Thus, in each of the 13 folders, each PNG image has two annotation files. E.g.,,2020_02_27_RR_Hive_Directions_IMG_2540_VK.PNG,2020_02_27_RR_Hive_Directions_IMG_2540_VK.xml,2020_02_27_RR_Hive_Directions_IMG_2540_VK.txt,Each PNG is annotated for the following categories:,(1) CappedHoneyCell,(2) CappedWorkerBroodCell,(3) EmptyCombCell,(4) PollenCell,(5) UncappedNectarCell,(6) UncappedWorkerLarvaCell,(7) BeeHiveFrame,The counts on the number of annotated region of interest (ROI) images are as follows:,CappedHoneyCell: 19,723,CappedWorkerBroodCell: 21,456,EmptyCombCell: 20,655,PollenCell: 13,406,UncappedNectarCell: 11,009,UncappedWorkerLarvaCell: 18,283,BeeHiveFrame: 1001,Each such ROI can be extracted into a separate image and used in training machine learning algorithms.,The subdirectory SRC/ contains two Python scripts that can convert XML to TXT and TXT to XML: xml_to_txt_converter.py and txt_to_xml_converter.py.,USDA_ARZ_DATA_YOLO_19june2025.zip is a 3GB zip version of these images prepared for YOLO training. It is available at https://usu.box.com/s/dh75xkinwfyl3sqgb9vugy1ahf6z9mrh.,SRC/ also contains the following Python scripts that we used for training YOLO networks:,(a) train_valid_split.py -- splits all alldata.txt in USDA_ARZ_DATA_YOLO_19june2025.zip into train.txt and valid.txt for YOLO training.,(b) tune_y8n.py --- tunes YOLOv8-nano,(c) tune_y8s.py --- tunes YOLOv8-small,(d) tune_y11n.py -- tunes YOLOv11-nano,(e) tune_y11s.py -- tunes YOLOv11-small,The folder METADATA/ contains two files: METADATA.txt and PapersDataSets_DrMeikle.xlsx. These files provide the metadata on the the USDA-ARS Tucson, AZ reservoir.,

Survey data collected in 2016. This project will construct a knowledge base using data on management practices, health, and production of honeybee colonies, supplied by beekeepers themselves, mainly via text messaging. These data, combined with GIS technology to map hive locations and foraging landscapes, will be used to identify the best management practices and most productive landscapes for honeybees.

AI를 활용하여 국내 발새 해충을 조기에 발견하여 신속하고 효과적인 초기 대응이 가능하기 위하여 자동화된 해충 감지 및 분류가 가능할 수 있는 양질의 학습용 데이터를 구축하고 이를 개방하여 해충으로 인해 발생되는 농가 피해를 최소화하기 위한 체계구축의 기반을 마련하고자 함

충청남도 농업기술우수연구결과를 공유하는 내용입니다. 영농기술정보, 신품종개발, 지식재산권 및 정책제안 등을 제공합니다.

This project will construct a knowledge base using data on management practices, health, and production of honeybee colonies, supplied by beekeepers themselves, mainly via text messaging. These data, combined with GIS technology to map hive locations and foraging landscapes, will be used to identify the best management practices and most productive landscapes for honeybees.