Nasr, Noureddine (author), Hdaidi, El Ayech (author), and Ayed, Ali Ben (author)
Format:
Book chapter
Publication Date:
2001
Published:
Tunisia
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: D01210
Notes:
Pages 293-299 in Waters-Bayer (eds.), Farmer innovation in Africa: a source of inspiration for agricultural development. Earthscan Publications, Ltd., London, England. 362 pages.
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.
O'Gorman, Melanie (author / University of Toronto) and Centre for the Study of African Economics, Oxford, UK
Format:
Conference paper
Publication Date:
2006-07-20
Published:
United Kingdom
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: C28215
Notes:
Posted online at http://www.csae.ox.ac.uk/conferences/2007-EDiA-LaWBiDC/papers/295-OGorman.pdf, Presented at the "Economic development in Africa" conference from March 18-20, 2007 at Oxford University.
