Data/hora: 16/06/2024 09:54:30

Biblioteca(s): 	Embrapa Algodão.	
Data corrente: 	21/08/2023	
Data da última atualização: 	21/08/2023	
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.	
Título: 	Soil and satellite remote sensing variables importance using machine learning to predict cotton yield.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Smart Agricultural Technology, v. 5, p. 1-10, 100292, 2023.	
ISSN: 	2772-3755	
DOI: 	https://doi.org/10.1016/j.atech.2023.100292	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Árvores de decisão; Decision trees; Imagem de satélite; Inteligência artificial; Produção sustentável; Proximal sensors; Random forest; RS; Satellite imagery; Sensores proximais; Sustainable production.	
Thesagro: 	Algodão; Estrutura do Solo; Gossypium Hirsutum; Sensoriamento Remoto.	
Thesaurus Nal: 	Artificial intelligence; Cotton; Remote sensing; Soil structure.	
Categoria do assunto: 	F Plantas e Produtos de Origem Vegetal	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1156016/1/SOIL-SATELLITE-COTTON-ZIANY.pdf	
Registro original: 	Embrapa Algodão (CNPA)	
Exemplares: 	CNPA - (29080-1) - UPC - AP - DD -  -  -  - DIS