Milne, Alice E. (author), Glendining, Margaret J. (author), Lark, Murray (author), Perryman, Sarah A.M. (author), Gordon, Taylor (author), and Whitmore, Andrew P. (author)
Format:
Journal article
Publication Date:
2015-09-01
Published:
United Kingdom: Elsevier
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 162 Document Number: D07993
Huysveld, Sophie (author), Van Meensel, Jef (author), Van linden, Veerle (author), De Meester, Steven (author), Peiren, Nico (author), Muylle, Hilde (author), Dewulf, Jo (author), and Lauwers, Ludwig (author)
Format:
Journal article
Publication Date:
2017-01
Published:
Belgium: Elsevier
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 161 Document Number: D07798
New-Aaron, Moses (author), Semin, Jessica (author), Duysen, Ellen G. (author), Madsen, Murray (author), Musil, Kelsie (author), and Rautiainen, Risto H. (author)
Format:
Journal article abstract
Publication Date:
2019
Published:
USA: Taylor & Francis
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 25 Document Number: D10537
8 pages., via online journal., The Bureau of Labor Statistics (BLS) publishes annual statistics on occupational injuries and fatalities in the United States. The BLS fatality data include all agricultural workers while the non-fatal injury data only cover hired employees on large farms. In 2012, the Central States Center for Agricultural Safety and Health (CS-CASH) began collecting regional media monitoring data of agricultural injury incidents to augment national statistics. The aims of this report were: a) to compare CS-CASH injury and fatality data collected via print and online sources to data reported in previous studies, and b) to compare fatality data from media monitoring to BLS Census of Fatal Occupational Injuries (CFOI) data. CS-CASH media monitoring data were collected from a news clipping service and an internet detection and notification system. These data covered years 2012–2017 in seven Midwestern states (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota). CS-CASH occupational fatality data were compared with aggregate CFOI data for the region during 2012–2015. Media monitoring captured 1048 injury cases; 586 (56%) were non-fatal and 462 (44%) were fatal. The numbers of occupational fatality cases from media monitoring and CFOI were nearly identical (280 vs. 282, respectively), and the distributions by type of injury were similar. Findings suggest that media monitoring can capture equal numbers of fatalities compared to CFOI. Non-fatal injuries, not captured by national surveillance systems, can be collected and tracked using print and electronic media. Risk factors, identified in media sources, such as gender, age, time, and source of the incident are consistent with previously reported data. Media monitoring can provide timely access to detailed information on individual cases, which is important for detecting unique and emerging hazards, designing interventions and for setting policy and guiding national strategies.
19 pages., Online via UI e-subscription, Authors collected consumer data to understand the heterogeneity of consumer behavior and store competition in grocery shopping. Marketing research techniques were used to analyze consumers' decision processes and their preference models.
17 pages, Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.
