Estimating Chlorophyll Content, Production, and Quality of Sugar Beet under Various Nitrogen Levels Using Machine Learning Models and Novel Spectral Indices

Estimating Chlorophyll Content, Production, and Quality of Sugar Beet under Various Nitrogen Levels Using Machine Learning Models and Novel Spectral Indices

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorop...

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Bibliographic Details
Main Authors: Salah Elsayed, Salah El-Hendawy, Osama Elsherbiny, Abdelaziz M. Okasha, Adel H. Elmetwalli, Abdallah E. Elwakeel, Muhammad Sohail Memon, Mohamed E. M. Ibrahim, Hazem H. Ibrahim
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Agronomy
Subjects:
gradient boosting regression
non-destructive
precision agriculture technology
root yield
sugar yield
sugar content
Online Access:https://www.mdpi.com/2073-4395/13/11/2743

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https://www.mdpi.com/2073-4395/13/11/2743

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