22 pages, The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.
Arnot, Charlie (author / Center for Food Integrity)
Format:
Commentary
Publication Date:
2020
Published:
International: Center for Food Integrity, Gladstone, Missouri.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 201 Document Number: D11706
Notes:
4 pages., Online from publisher website., Perspectives about how consumers will perceive technology in food and agriculture going forward. "...will they view innovation as positive and something they should embrace and support? Or, will innovation be perceived as another looming threat that should be avoided at all costs? The answer to those questions rests with those who bring the technology to market."
20 pages., Via online journal., Agricultural technology continues to evolve to meet the demands of a growing world, but previous advancements in agricultural technology have been met with resistance. Improved science communication efforts can assist in bridging the gap between expert and lay opinion to improve reception of scientific information. Using the framework of the heuristic model of persuasion, the purpose of this study was to examine the impact of emphasizing elements of source credibility – trustworthiness and expertise – and the gender of the source on perceptions of source credibility. A sample of 122 undergraduate students were exposed to one of the four possible developed message treatments. Data collection took place in a laboratory setting using an online instrument that had a randomly-assigned stimulus research design. The results indicated the treatment conditions had higher mean scores for source credibility than the control. Further inferential analysis, however, showed the differences to be non-significant. One significant finding showed the gender of the source can influence perceptions of credibility. This suggests merit in using female sources when presenting scientific information to the Millennial population. While choosing credible sources to present information is important, more research is needed regarding the effect of emphasizing various credibility components and the role of source gender on perceptions of source credibility.
15pgs, Agriculture is crucial in catering to the increasing demand for food and employment. Thus, adoption of novel technologies is important. Many scientists have developed different theories and models explaining the process of behavioral change relevant to adoption. They are either completely different, similar, or improvements of previously developed models. Therefore, compilation and summarization of these theories and models will support future studies and researchers. Thus, an analysis of literature on technology adoption was conducted. The review was prepared based on literature from various sources spanning around 50 years. The theories and models identified by different studies were compiled and analyzed in this review paper. Many theories and models in agricultural technology adoption such as transtheoretical model, theory of reasoned action, theory of interpersonal behavior, model for innovation-decision process, different versions of technology acceptance model, theory of planned behavior, theory of diffusion of innovation, task-technology fit, technology readiness, unified theory of acceptance and use of technology, expectancy livelihood model, social cognitive theory, and perceived characteristics of innovating theory were compiled. Each theory and model has its own uniqueness, which had explained different aspects of technology adoption process and factors determining the behavioral change. These theories and models included affecting factors such as technological, personal, social, and economical factors. In conclusion, it can be stated that, rather than having a single theory or a model, an integrated and amalgamated form will be more explanatory for technology adoption.
