Titlebook: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector; Vitor Joao Pereira Domingues Martinho Book

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书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector影响因子(影响力)




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector影响因子(影响力)学科排名




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector网络公开度




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector网络公开度学科排名




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector被引频次




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector被引频次学科排名




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector年度引用




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector年度引用学科排名




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector读者反馈




书目名称Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector读者反馈学科排名

弄皱

978-3-031-54607-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

NEG

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Vitor Joao Pereira Domingues MartinhoShows how to identify the crucial variables needed to solve agricultural production unit management challenges.Contains many tables and diagrams to illustrate the book‘s message.Useful to students, pu

河流

SpringerBriefs in Applied Sciences and Technologyhttp://image.papertrans.cn/m/image/620385.jpg

JEER

Book 2024ging 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 t

针叶

Predictive Machine Learning Approaches to Agricultural Output, European Union farming output, taking into account machine learning approaches and statistical information from the Farm Accountancy Data Network. The results obtained highlight the most important farming variables that must be taken into account to predict the total output in the European Union farms.

你敢命令

Applying Artificial Intelligence to Predict Crop Output, Network were considered, as well as approaches associated with artificial intelligence. The main findings provide relevant insights and knowledge, namely for farmers and policymakers that may be considered in the processes of agricultural planning, management and policy design.

FOLLY

Predictive Machine Learning Models for Livestock Output, to suggest models and predictors to support the farmers and other stakeholders to better design policies and farm plans. Statistical information from the European Union databases was considered. The results found are useful tools to improve the performance of the European Union farms, particularly those specialised in livestock production.

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