Titlebook: Data Science in Agriculture and Natural Resource Management; G. P. Obi Reddy,Mehul S. Raval,Sanjay Chaudhary Book 2022 The Editor(s) (if a

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書目名稱Data Science in Agriculture and Natural Resource Management影響因子(影響力)




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書目名稱Data Science in Agriculture and Natural Resource Management網(wǎng)絡公開度學科排名




書目名稱Data Science in Agriculture and Natural Resource Management被引頻次




書目名稱Data Science in Agriculture and Natural Resource Management被引頻次學科排名




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書目名稱Data Science in Agriculture and Natural Resource Management年度引用學科排名




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書目名稱Data Science in Agriculture and Natural Resource Management讀者反饋學科排名

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Gerhard X. Ritter,Gonzalo Urcidus spatial data, and integration of these datasets with ground-based observational data significantly enhanced the capabilities in mapping, monitoring, and forecasting various earth system processes. ML algorithms have vast potential in monitoring earth resources to provide timely information for si

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Vassilis G. Kaburlasos,Gerhard X. Ritteright amount of pesticide, irrigation water, and nutrients. The typical components of Precision Farming include background data, record-keeping systems, data analysis, and decision-making process, specialized implementation equipment, and evaluation and revision. Significant benefits of precision agr

重疊

Angelos Barmpoutis,Gerhard X. Ritter by highlighting their computational efficiency and higher model interpretability in comparison with other earlier mentioned statistical learning techniques. This is followed by the introduction of basic terms and concepts necessary for understanding DT models and their associated statistical learni

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José Luis Verdegay,Julio Brito,Carlos Cruztes which are required by the specific consumer. The recommendations can be generated by using the endogenous and exogenous data captured through IoT sensors and AI modeling. The architecture is generic and solutions can be designed to work for a range of foods/crops. The architecture is designed to

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Simultaneous Phasing of Multiple Polyploidsusefulness of the current data sources and methods, this chapter presents a methodology that combines seasonal weather forecasts, geo-spatial information derived from remote-sensing, risks posed by extreme events and crop growth models to estimate production risk at a regional scale. The method was

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https://doi.org/10.1007/978-3-319-24462-4asses, the clustering algorithms to group the objects into classes based on a given set of input variables, the regression algorithms to forecast a response variable from a given a set of covariates, and the dimensionality reduction algorithms to build a small set of new variables that includes most