Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters-Reference-Cited by-同舟云学术

Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters

Published:2024-01-03 Issue:1 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Datt Rakesh Mohan^ORCID,Kukreja Vinay

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-02463-z.pdf

Reference31 articles.

1. FAOSTAT Apple Crop database. Published online 2022. https://www.fao.org/faostat/en/#data/QCL. Accessed 3 July 2023.

2. Kour K, Gupta D, Gupta K, Juneja S, Kaur M, Alharbi AH, Lee H-N. Controlling agronomic variables of saffron crop using IoT for sustainable agriculture. Sustainability. 2022;14(9):5607.

3. Kour K, Gupta D, Gupta K, Anand D, Elkamchouchi DH, Pérez-Oleaga CM, Ibrahim M, Goyal N. Monitoring ambient parameters in the IoT precision agriculture scenario: an approach to sensor selection and hydroponic saffron cultivation. Sensors. 2022;22:8905.

4. Li M, Guo J, He J, et al. Possible impact of climate change on apple yield in Northwest China. Theoret Appl Climatol. 2020;139(1–2):191–203.

5. RK Pachauri LA Meyer 2014 Climate change 2014: Synthesis report contribution of working groups I II and III to the Fifth assessment report of the intergovernmental panel on climate change IPCC Geneva Switzerland 2014 151

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design and Development of an Apple Fruit Disease Identification Methodology Based on Enhanced Deep Learning Strategy;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04