Journal Article10.1016/J.COMPAG.2018.03.010
Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems
Xianming Dou,Yongguo Yang +1 more
146
TL;DR: The advanced ELM and ANFIS models can be recommended as important complements to traditional methods due to their robustness and flexibility and significant difference regarding the modeling performance existed among the four major ecosystems types.
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About: This article is published in Computers and Electronics in Agriculture. The article was published on 01 May 2018. The article focuses on the topics: Adaptive neuro fuzzy inference system & Extreme learning machine.
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Citations
Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
TL;DR: In this paper, three different evapotranspiration models have been compared in an experimental site in Central Florida, characterized by humid subtropical climate, and four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression.
275
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Dhivya Elavarasan,P. M. Durai Raj Vincent,Vishal Sharma,Albert Y. Zomaya,Kathiravan Srinivasan +4 more
TL;DR: This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature and compares one approach with other using various error measures like Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
260
Remote sensing and machine learning for crop water stress determination in various crops: a critical review
TL;DR: This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.
237
Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
Shufen Pan,Naiqing Pan,Naiqing Pan,Hanqin Tian,Pierre Friedlingstein,Stephen Sitch,Hao Shi,Vivek K. Arora,Vanessa Haverd,Atul K. Jain,Etsushi Kato,Sebastian Lienert,Danica Lombardozzi,Julia E. M. S. Nabel,Catherine Ottlé,Benjamin Poulter,Sönke Zaehle,Steven W. Running +17 more
TL;DR: In this article, the authors reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs).
Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
TL;DR: This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem and discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.
References
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Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
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TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
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ANFIS: adaptive-network-based fuzzy inference system
Jyh-Shing Roger Jang
- 01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
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Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
11.6K