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."