Improvement of agricultural productivity and optimization of rural economic structure from a digital perspective
Author:
Affiliation:
1. PingDingShan University , Pingdingshan, Henan , , China .
2. HuangHuai University, Zhumadian , Henan , , China .
Abstract
Publisher
Walter de Gruyter GmbH
Link
https://www.sciendo.com/pdf/10.2478/amns-2024-2033
Reference24 articles.
1. Mitchell, S., Weersink, A., & Erickson, B. (2018). Adoption of precision agriculture technologies in ontario crop production. Canadian Journal of Plant Science, 98, 1384 - 1388.
2. Freeman, K., Valencia, V., Marzaroli, J., & Zanten, H. V. V. (2022). Digital traceability to enhance circular food systems and reach agriculture emissions targets. Outlook on Agriculture, 51, 414 - 422.
3. Jamil, A. B. B. (2021). The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods. Geocarto international, 36(5a8).
4. Ning, J., Liu, J., Kuang, W., Xu, X., & Ning, J. (2018). Spatiotemporal patterns and characteristics of land-use change in china during 2010–2015. Journal of Geographical Sciences, 28(5), 547-562.
5. Balaji, V., Raymond, J. W., & Pritam, C. (2018). Deepsort: deep convolutional networks for sorting haploid maize seeds. BMC Bioinformatics, 19(S9), 85-93.
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