Título:	Soil and satellite remote sensing variables importance using machine learning to predict cotton yield.
Autoria:	CARNEIRO, F. M. BRITO FILHO, A. L. de FERREIRA, F. M. SEBEN JUNIOR, G. de F. BRANDÃO, Z. N. SILVA, R. P. da SHIRATSUCHI, L. S.
Afiliação:	FRANCIELE MORLIN CARNEIRO, UTFPR; ARMANDO LOPES DE BRITO FILHO, UNESP; FRANCIELLE MORELLI FERREIRA, UNESP; GETULIO DE FREITAS SEBEN JUNIOR, UNEMAT; ZIANY NEIVA BRANDÃO, CNPA; ROUVERSON PEREIRA DA SILVA, UNESP; LUCIANO SHOZO SHIRATSUCHI, LOUISIANA STATE UNIVERSITY.
Ano de publicação:	2023
Referência:	Smart Agricultural Technology, v. 5, p. 1-10, 100292, 2023.
Conteúdo:	Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R2), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield.
Thesagro:	Algodão Estrutura do Solo Sensoriamento Remoto Gossypium Hirsutum
NAL Thesaurus:	Artificial intelligence Cotton Soil structure Remote sensing
Palavras-chave:	Produção sustentável Sensores proximais Random forest Satellite imagery Sustainable production Proximal sensors Inteligência artificial Imagem de satélite RS Decision trees Árvores de decisão
ISSN:	2772-3755
Digital Object Identifier:	https://doi.org/10.1016/j.atech.2023.100292
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPA)