• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 춘계학술대회
• /
• pp.194-196
• /
• 2021

본 논문에서는 스마트농업의 생산 전 단계에서 생산 계획을 세워야 하는 농업 생산자와 유통사업자들에게 농업 정보 데이터를 제공하고 빅 데이터 분석에서 요구되는 다량의 데이터를 처리하고 분석할 수 있는 형태로 변환시켜 분석에 적용하는 스마트 농업 생산 전 서비스 모델을 제시한다. 수집된 데이터들을 저장 및 추출하기 위한 농업 정보 베이스의 구축 그리고 농업생산자와 유통사업자의 질의에 대응하여 적절한 자문을 수행할 수 있게 해주는 상호 소통 수단에 대한 기준 구조도 제시하였다.

• Safety and Health at Work
• /
• 제14권1호
• /
• pp.50-58
• /
• 2023

Background: Occupational health services are not available for more than 80% of the global workforce. This pertains especially to informal workers, workers in agriculture and in small enterprises, and self-employed. Many are working in hazardous conditions. The World Health Organization, the International Labor Organization, the International Commission on Occupational Health, and the World Organization of Family Doctors promote as part of a solution, basic occupational health services (BOHS) integrated in primary or community health care. Quality information on this topic is difficult to find. The objective of this study is to develop an open access bibliography, a repository, referring to publications on BOHS and similar innovations, to support progress and research. Methods: The database design and sustaining literature searches (PubMed, Google Scholar, SciELO) are described. For each publication selected, basic bibliographic data, a brief content description considering copyright restrictions, and a hyperlink are included. Results: Searches resulted in a database containing 189 references to publications on BOHS such as articles in scientific journals, reports, policy documents, and abstracts of lectures. A global perspective is applied in 43 publications, a national or regional perspective is applied in 146 publications. Operational and evaluative research material is still scarce. Examples of references to publications are shown. Conclusion: The repository can inspire pioneers by showing practices in different countries and can be used for reviews and in-depth analyses. Missing publications such as from China, Russia, Japan, Republic of Korea, and Spanish/Portuguese speaking countries, can be added in the future, and translated. Search functions can be developed. International collaboration for the promotion of occupational health coverage for all workers must be intensified.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.959-979
• /
• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.