Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings
Autor: | Gabriela Karoline Michelon, Erminio Pita Jasse, Paulo Lopes de Menezes, Paulo Sérgio Graziano Magalhães, Ligia Francielle Borges, Claudio Leones Bazzi |
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Rok vydání: | 2018 |
Předmět: |
0106 biological sciences
Chlorophyll content Artificial neural network Physiology fungi food and beverages Growing season macromolecular substances 04 agricultural and veterinary sciences 01 natural sciences Crop productivity chemistry.chemical_compound chemistry Agronomy Chlorophyll 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Precision agriculture Agronomy and Crop Science Productivity Plant nutrition 010606 plant biology & botany |
Zdroj: | Journal of Plant Nutrition. 41:1285-1292 |
ISSN: | 1532-4087 0190-4167 |
Popis: | Crop productivity prediction techniques assist with adjusting for potential agronomic problems during the growing season. Several authors have reported that there is a correlation between leaf chlo... |
Databáze: | OpenAIRE |
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