Name: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (en Inglés)
Price: 1166.75 MXN
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Author: Martinho, Vitor Joao Pereira Domingues
ISBN: 9783031546075

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portada Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (en Inglés)

Formato

Libro Físico

Autor

Vítor João Pereira Domingues Martinho

Editorial

Springer

Idioma

Inglés

N° páginas

135

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.8 cm

Peso

0.22 kg.

ISBN13

9783031546075

N° edición

2024th

Categorías

Control de calidad y gestión de la productividad
Producción de alimenos
Inteligencia artificial

Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (en Inglés)

Vítor João Pereira Domingues Martinho (Autor) · Springer · Tapa Blanda

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Reseña del libro "Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (en Inglés)"

This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector.