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.
Summarizes results of a non-farmer survey documenting how each of five stages of the agricultural and food business value chain is evolving in terms of data collection and use.
4 pages, via Online journal, Bougainvillea (Bougainvillea sp.) plant inflorescence number will vary in response to multiple cues such as changes in temperature, water, light intensity, pruning, and photoperiod. Previous research reports that the application of plant growth regulators (PGRs) to bougainvillea grown under varying photoperiods improved inflorescence number, probably as a result of changes in gibberellic acid (GA) levels. There are many bioactive plant GAs, but we chose to investigate differences in gibberellic acid 3 (GA3) levels and inflorescence number in response to the application of ethephon (2-cholorethylphosponic acid) or abscisic acid (ABA) to ‘Afterglow’ bougainvillea (Bougainvillea ×buttiana) grown under 14-hour photoperiod [long-day (LD)] conditions. Plants were 5 inches tall with seven visible lateral nodes and were grown in a greenhouse in 4-inch pots filled with 5-mm coarse aquarium zeolite. Ethephon was applied as a foliar spray at 0.05, 0.07, 0.10, 0.15, or 0.20 mg/plant. ABA was applied as a soil drench at 1, 1.5, 3, 6, 8, or 10 mg/plant. Endogenous levels of GA3 were measured 1 and 48 days after treatment to calculate the change in GA3 (∆GA3). A short day (SD) control of 8 hours was included to measure differences in inflorescence number and ∆GA3 between photoperiods. ‘Afterglow’ plants grown under SD conditions had the greatest decrease in ∆GA3 (–1.09 µg·g–1) over 48 days and the most inflorescences (10.6) compared with LD control plants with a decrease in ∆GA3 of –0.09 µg·g–1 and fewer inflorescences (1.0). Plants grown under LD conditions and treated with 0.05 mg/plant ethephon had inflorescence numbers (9.6) and levels of ∆GA3 (–0.74 µg·g–1) similar to the SD control. As ethephon rate increased to more than 0.05 mg/plant, inflorescence number on LD plants decreased and ∆GA3 increased. Exogenous ABA rates of 1 mg/plant produced inflorescence numbers (1.4) and ∆GA3 (–0.10 µg·g–1) similar to the LD control. As the rate increased, ∆GA3 increased and inflorescence number decreased. Plants treated with ABA rates of 3 mg/plant and more were defoliated and had no inflorescences.
16 pages., Via online journal., Expertise is dynamic, domain specific, and characterized according to an individual’s level of knowledge, experience, and problem-solving ability. Having expertise in the phenomenon under investigation can be used as an indicator of an individual’s aptitude to effectively serve as a coder in a content analysis or as panelist in a Delphi study. The purpose of this study was to assess 10 years of scholarship published in the premier journals of agricultural education and describe the ways researchers in agricultural communications, education, extension, and leadership disciplines who use content analysis and Delphi study methods are describing the qualifications of the people serving as expert coders and panelists. The study findings revealed the majority of researchers publishing in the premier agricultural education journals are not describing the qualifications used in selecting coders or the credentials the coders possess that would make them qualified to code the data in a content analysis. Furthermore, researchers were inconsistent citing literature that supported their selection of content analysis coders and citing literature to support a decision to describe or not to describe coders’ qualifications. However, a description of Delphi study panelists’ qualifications and citations to support why panelists were selected in a Delphi study were present in all of the Delphi studies analyzed over the 10-year period. Based on these findings, it was concluded that ACEEL researchers should include a description of coder credentials to enhance the consistency, transparency, replicability, rigor, and integrity of ACEEL research. Editors and research professionals who perform journal article reviews for the premier agricultural education journals are encouraged to note the exclusion of a description of content analysis coders’ credentials as part of the peer review process.
Dorward, Andrew R. (author), Rehman, T. (author), and University of Reading, Reading, Great Britain, Dept of Agriculture and Horticulture; University of Reading, Reading, Great Britain, Dept of Agriculture and Horticulture
Format:
Journal article
Publication Date:
1984
Published:
UK
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 52 Document Number: C00595
