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.
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: D02420
Notes:
Page 33 - Abstract of a paper presented at the International Conference of the Australasia Pacific Extension Network (APEN), Lincoln University, Christchurch, New Zealand, August 26-28, 2013. 100 pages.
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 141 Document Number: D06191
Notes:
Locate in file folder for Document No. D06187, Pages 56-59 in L. Johnson, Alhassan WS Anthony V. and P. Rudelsheim (eds.), 2011. Agricultural biotechnology in Africa: stewardship case studies. Forum for Agricultural Research in Africa, Accra,Ghana. 60 pages., Authors emphasize the importance of having integrated communication and awareness training programmes for all players in the product life cycle.
