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.
Madushanka, L.S. (author), Weerasinghe, K.S. (author), Weerakkody, W.J.S.K. (author), and Department of Plantation Management, Faculty of Agriculture and Plantation Management, Wayamba University of Sri Lanka, Makandura, Gonawila (NWP), Sri Lanka
ICT Center, Wayamba University of Sri Lanka, Makandura, Gonawila (NWP), Sri Lanka
Format:
Conference paper
Publication Date:
2017-01-23
Published:
Sri Lanka: Institute of Electrical and Electronics Engineers Inc.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 162 Document Number: D08140
Notes:
16th International Conference on Advances in ICT for Emerging Regions, ICTer 2016; Jetwing BlueNegombo; Sri Lanka; 1 September 2016 through 3 September 2016; Category numberCFP1686L-ART; Code 126111. Article number 7829902, pp. 80-86
Jansen, Guido (author), Cila, Nazli (author), Kanis, Marije (author), and Slaats, Yanti (author)
Format:
Paper
Publication Date:
2016-05
Published:
USA: Association for Computing Machinery
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 93 Document Number: D10858
Notes:
Conference on Human Factors in Computing Systems - Proceedings Volume 07-12-May-2016, Pages 3091-3098. 34th annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016,, San Jose, California., Vertical farming is a promising new technology for increasing crop yields per square meter. However, little research has been done so far in people's perception of this technology. The aim of this project was to gain a better understanding of consumers' attitude on small scale vertical farming at home. This was achieved by developing a prototype that uses sensor and LED technology for growing food at home and deploying it in a user study. The prototype was built to give users a genuine feeling of what it would be like to use a small scale vertical farming system. The user study showed that the attitudes towards the system were mostly positive. However, a fully autonomous system is not desirable and there are concerns regarding food safety.
