Investigation of genetic diversity of different spring rapeseed (Brassica napus L.) genotypes and yield prediction using machine learning models

Norouzi, Mohamad Amin; Ahangar, Leila; Payghamzadeh, Kamal; Sabouri, Hossein; Sajadi, Sayed Javad

doi:10.1007/s10722-024-01915-6

Genet Resour Crop Evol

2024

DOI: 10.1007/s10722-024-01915-6

|View full text |Cite

Investigation of genetic diversity of different spring rapeseed (Brassica napus L.) genotypes and yield prediction using machine learning models

Mohamad Amin Norouzi,

Leila Ahangar,

Kamal Payghamzadeh

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

Piekutowska,

Hara,

Pentoś

et al. 2024

Agronomy

View full text Add to dashboard Cite

Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.

show abstract

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

Piekutowska,

Hara,

Pentoś

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Investigation of genetic diversity of different spring rapeseed (Brassica napus L.) genotypes and yield prediction using machine learning models

Cited by 1 publication

References 35 publications

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

Contact Info

Product

Resources

About