Predictive framework of plant height in commercial cotton fields using a remote sensing and machine learning approach

Author:

da Silva Andrea Maria CarolinaORCID,de Oliveira Nascimento João Pedro F.,Mota Fabrícia Conceição Menez,de Souza Oliveira Rodrigo

Publisher

Elsevier BV

Reference48 articles.

1. Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models;Adnan;PeerJ Comput. Sci.,2022

2. Variable rate spraying application on cotton using an electronic flow controller;Baio;Precis. Agric.,2018

3. Performance metrics (error measures) in machine learning regression, forecasting and prognostics: properties and typology;Botchkarev;Interdiscip. J. Inf. Knowl. Manag.,2019

4. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation;Chicco;PeerJ Comput. Sci.,2021

5. Companhia Nacional de Abastecimento [CONAB] 2022. Available at: https://www.conab.gov.br/info-agro/safras/serie-historica-das-safras/itemlist/category/898-algodao. Accessed on 10 Jun 2022.

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