Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon-Reference-Cited by-同舟云学术

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Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon

Published:2020-10 Issue: Volume:177 Page:105710
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Costa José Janderson Ferreira,Giasson Élvio,da Silva Elisângela Benedet,Coblinski João Augusto,Tiecher Tales

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference99 articles.

1. Prediction of soil characteristics and colour using data from the National Soils Inventory of Scotland;Aitkenhead;Geoderma,2013

2. E-SMART: environmental sensing for monitoring and advising in real-time;Aitkenhead,2013

3. Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy;Allo;Catena,2020

4. Quantification of soil organic carbon stock in urban soils using visible and near infrared reflectance spectroscopy (VNIRS) in situ or in laboratory conditions;Allory;Sci. Total Environ.,2019

5. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques;Araújo;Eur. J. Soil Sci.,2014

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1. Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction;Soil Science Society of America Journal;2024-05-16

2. Iron-removal learning machine for multicolor determination of soil organic carbon;Journal of Soils and Sediments;2024-03-12

3. Mapping Soil Organic Matter Under Field Conditions;IEEE Transactions on AgriFood Electronics;2024-03

4. Soil organic matter content prediction based on two-branch convolutional neural network combining image and spectral features;Computers and Electronics in Agriculture;2024-02

5. Evaluating mobile applications for estimating soil properties: Quality of current apps, limitations and future directions;Computers and Electronics in Agriculture;2024-01

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