Classification model for chlorophyll content using CNN and aerial images-Reference-Cited by-同舟云学术

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Classification model for chlorophyll content using CNN and aerial images

Published:2024-06 Issue: Volume:221 Page:109006
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Wagimin Mohd Nazuan,Ismail Mohammad Hafiz bin,Fauzi Shukor Sanim Mohd,Seng Chuah Tse,Latif Zulkiflee Abd,Muharam Farrah Melissa,Zaki Nurul Ain Mohd

Funder

Universiti Teknologi MARA

Malaysia Ministry of Higher Education

Publisher

Elsevier BV

Reference67 articles.

1. Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model;Abdalla;IEEE Trans. Ind. Inf.,2021

2. Feature extraction of plant leaf using deep learning;Ahmad;Complexity,2022

3. Deep learning-based model for classification of bean nitrogen status using digital canopy imaging;Baesso;Engenharia Agrícola,2023

4. Rapid monitoring of nitrogen in mango trees;Bally;Acta Hortic.,2013

5. Nondestructive and rapid estimation of hardwood foliar nitrogen status using the SPAD-502 chlorophyll meter;Chang;For. Ecol. Manage.,2003

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