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.
McDowell, George R. (author / University of Massachusetts, Amherst, MA)
Format:
Conference paper
Publication Date:
1987-09
Published:
USA
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 87 Document Number: C05779
Notes:
AGRICOLA IND 88063091, In: Luloff, Albert E., ed. Proceedings of the rural people and places : a symposium on typologies; 1986 October 22-24; Grantville, PA. University Park, PA : The Northeast Regional Center for Rural Development, 1987. p. 150-159
13 pages, via Online Journal, This paper contributes to our understanding of farm data value chains with assistance from 54 semi-structured interviews and field notes from participant observations. Methodologically, it includes individuals, such as farmers, who hold well-known positionalities within digital agriculture spaces—platforms that include precision farming techniques, farm equipment built on machine learning architecture and algorithms, and robotics—while also including less visible elements and practices. The actors interviewed and materialities and performances observed thus came from spaces and places inhabited by, for example, farmers, crop scientists, statisticians, programmers, and senior leadership in firms located in the U.S. and Canada. The stability of “the” artifacts followed for this project proved challenging, which led to me rethinking how to approach the subject conceptually. The paper is animated by a posthumanist commitment, drawing heavily from assemblage thinking and critical data scholarship coming out of Science and Technology Studies. The argument’s understanding of “chains” therefore lies on an alternative conceptual plane relative to most commodity chain scholarship. To speak of a data value chain is to foreground an orchestrating set of relations among humans, non-humans, products, spaces, places, and practices. The paper’s principle contribution involves interrogating lock-in tendencies at different “points” along the digital farm platform assemblage while pushing for a varied understanding of governance depending on the roles of the actors and actants involved.
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 171 Document Number: C28720
Notes:
Presented at the World Conference on Agricultural Information and IT (IAALD-AFITA-WCCA2008), Tokyo University of Agriculture, Japan, August 2008. 11 pages.
