Algorithms for forecasting cotton yield based on climatic parameters in Brazil
| Autor: | Tatiana da Silva Santos, Washington Bruno Silva Pereira, Paulo Alexandre da Silva, José Reinaldo da Silva Cabral de Moraes, Mary Jane Nunes Carvalho, Lucas Eduardo de Oliveira Aparecido, Glauco Rolim de Souza, Kamila Cunha de Meneses |
|---|---|
| Přispěvatelé: | Federal Institute of Mato Grosso Do Sul – IFMS, Universidade Estadual Paulista (Unesp) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Artificial intelligence business.industry Yield (finance) Big data Soil Science deep learning 04 agricultural and veterinary sciences Agricultural engineering 01 natural sciences Random forest Water balance water balance Sustainability 040103 agronomy & agriculture 0401 agriculture forestry and fisheries cop modelling business Agronomy and Crop Science bigdata random forest 010606 plant biology & botany Mathematics |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| Popis: | Made available in DSpace on 2021-06-25T10:19:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Accurate forecasts of cotton yield are of great interest for the development of the market, increasing the sustainability of the sector worldwide. Thus, the objectives of this study were: 1) to evaluate the influence of climatic elements on cotton yield in Brazil, 2) to predict cotton yield using machine learning algorithms based on climatic elements, 3) to calibrate and test machine learning models to forecast cotton yield based on climate data, and 4) to interpolate the estimated cotton yield of the most accurate model. The cotton yield forecast as a function of climatic elements was performed using machine learning algorithms with four parameters adjusted by ordinary least squares. The models show that cotton yield has a sigmoid trend due to the accumulation of P, PET, STO, and EXC during the cycle. It is possible to forecast cotton yield for the main producing regions of Brazil using Machine learning algorithms. Extra-trees regressor models performed better in forecasting cotton yield using climatic data from planting to flowering. Therefore, it is possible to have average anticipation of around 80 days, allowing the producer time to plan his activities such as harvest and sales strategies. Department of Agricultural Engineering Federal Institute of Mato Grosso Do Sul – IFMS Campus of Naviraí Department of Engineering and Exact Sciences São Paulo State University – Unesp Department of Engineering and Exact Sciences São Paulo State University – Unesp |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|