Feature selection, artificial neural network prediction and experimental testing for predicting breakage rate of maize kernels based on mechanical properties-Reference-Cited by-同舟云学术

Feature selection, artificial neural network prediction and experimental testing for predicting breakage rate of maize kernels based on mechanical properties

Published:2020-12-13 Issue:2 Volume:44 Page:
ISSN:0145-8876
Container-title:Journal of Food Process Engineering
language:en
Short-container-title:J Food Process Eng

Author:

Xia Guoyi¹^ORCID,Xu Yang¹^ORCID,Su Yuan¹^ORCID,Gao Xiaojun¹^ORCID,Li Yibo¹,Qiao Mengmeng¹,Yu Yingbo¹

Affiliation:

1. College of Engineering, China Agricultural University Beijing PR China

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Wiley

Subject

General Chemical Engineering,Food Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jfpe.13621

Reference39 articles.

1. Environment, Management, and Genetic Contributions to Maize Kernel Hardness and Grain Yield

2. Current status of corn mechanical grain harvesting and its relationship with grain moisture content;Chai Z. W.;Agriculture Science in China,2017

3. Dynamic nanoindentation by instrumented nanoindentation and force microscopy: a comparative review

4. Apparent elastic modulus and hysteresis of skeletal muscle cells throughout differentiation

5. Research progress of maize mechanized harvesting technology;Cui T.;Transactions of the Chinese Society for Agricultural Machinery,2019

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