Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring-Reference-Cited by-同舟云学术

Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring

Published:2023-10-10 Issue:10 Volume:13 Page:1970
ISSN:2077-0472
Container-title:Agriculture
language:en
Short-container-title:Agriculture

Author:

Yue Jibo¹^ORCID,Zhou Chengquan²,Feng Haikuan³,Yang Yanjun⁴^ORCID,Zhang Ning⁵

Affiliation:

1. College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

2. Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

3. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China

4. Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY 40546, USA

5. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Abstract

The rapid development of intelligence and automated technologies has provided new management opportunities for agricultural production [...]

Funder

Henan Province Science and Technology Research Project

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

Link

https://www.mdpi.com/2077-0472/13/10/1970/pdf

Reference16 articles.

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4. Wang, Q., Guo, P., Dong, S., Liu, Y., Pan, Y., and Li, C. (2023). Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China. Agriculture, 13.

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