Machine learning and experimental testing for prediction of breakage rate of maize kernels based on components contents-Reference-Cited by-同舟云学术

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Machine learning and experimental testing for prediction of breakage rate of maize kernels based on components contents

Published:2022-11 Issue: Volume:108 Page:103582
ISSN:0733-5210
Container-title:Journal of Cereal Science
language:en
Short-container-title:Journal of Cereal Science

Author:

Qiao Mengmeng,Xia Guoyi,Cui Tao,Xu Yang,Fan Chenlong,Su Yuan,Li Yibo,Han Shaoyun

Funder

Agriculture Research System of China

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

Publisher

Elsevier BV

Subject

Biochemistry,Food Science

Reference29 articles.

1. Stepwise regression is an alternative to splines for fitting noisy data;Burkholder;J. Biomech.,1996

2. Environment, management, and genetic contributions to maize kernel hardness and grain yield;Cerrudo;Crop Sci.,2017

3. Current status of corn mechanical grain harvesting and its relationship with grain moisture content;Chai;Agricult. Sci. China,2017

4. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels;Esteve Agelet;J. Cereal. Sci.,2012

5. Power consumption and performance of a maize thresher with automatic gap control based on feed rate monitoring;Fan;Biosyst. Eng.,2022

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