Detecting heterogeneity parameters and hybrid models for precision farming

[1]  Yinjie J. Tang,et al.  A comparative evaluation of machine learning algorithms for predicting syngas fermentation outcomes , 2022, Biochemical Engineering Journal.

[2]  S. Sriboonchitta,et al.  Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand , 2022, Agriculture.

[3]  I. M. S. Wijaya,et al.  QUALITY CONTROL OF OPTICAL FIBER DISRUPTION WITH BIG DATA USING THE SIX SIGMA METHOD , 2022, JURTEKSI (Jurnal Teknologi dan Sistem Informasi).

[4]  Senthil Kumar Swami Durai,et al.  Smart farming using Machine Learning and Deep Learning techniques , 2022, Decision Analytics Journal.

[5]  Jinlong Fan,et al.  Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images , 2022, Remote. Sens..

[6]  Yuri Ariyanto,et al.  A Simple Approach using Statistical-based Machine Learning to Predict the Weapon System Operational Readiness , 2022, Proceedings of The International Conference on Data Science and Official Statistics.

[7]  Jesse T. Rieb,et al.  Farmland heterogeneity is associated with gains in some ecosystem services but also potential trade-offs , 2021, Agriculture, Ecosystems & Environment.

[8]  Kunshan Yao,et al.  A variable selection method based on mutual information and variance inflation factor. , 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[9]  Ahmad Fudholi,et al.  Accurate and Hybrid Regularization - Robust Regression Model in Handling Multicollinearity and Outlier Using 8SC for Big Data , 2021, Mathematical Modelling of Engineering Problems.

[10]  M. B. Senousy,et al.  A Solution for Handling Big Data Heterogeneity Problem , 2021, Digital Transformation Technology.

[11]  Matthijs J. Warrens,et al.  The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation , 2021, PeerJ Comput. Sci..

[12]  J. Ran,et al.  The effects of agricultural landscape composition and heterogeneity on bird diversity and community structure in the Chengdu Plain, China , 2020 .

[13]  S. Nabnean,et al.  Experimental and simulated investigations of the performance of the solar greenhouse dryer for drying cassumunar ginger (Zingiber cassumunar Roxb.) , 2020, Case Studies in Thermal Engineering.

[14]  S. Nabnean,et al.  Experimental performance of direct forced convection household solar dryer for drying banana , 2020 .

[15]  Majid Khan Majahar Ali,et al.  Ridge Regression as Efficient Model Selection and Forecasting of Fish Drying Using V-Groove Hybrid Solar Drier , 2020 .

[16]  N. Kumari,et al.  Hydrological Response to Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity Model , 2020, Water Resources Management.

[17]  A. Mujumdar,et al.  Importance of drying in support of human welfare , 2020, Drying Technology.

[18]  Rung-Ching Chen,et al.  Selecting critical features for data classification based on machine learning methods , 2020, Journal of Big Data.

[19]  B. K. Bala,et al.  Performance of a large-scale greenhouse solar dryer integrated with phase change material thermal storage system for drying of chili , 2020 .

[20]  Frits K. van Evert,et al.  Smart Farming Technology Trends: Economic and Environmental Effects, Labor Impact, and Adoption Readiness , 2020, Agronomy.

[21]  Andrej Kos,et al.  On the Interpretability of Machine Learning Models and Experimental Feature Selection in Case of Multicollinear Data , 2020, Electronics.

[22]  T. V. Arjunan,et al.  Exergo-environmental analysis of an indirect forced convection solar dryer for drying bitter gourd slices , 2020 .

[23]  Wen-Xue Hao,et al.  Mathematical Modeling and Performance Analysis of a New Hybrid Solar Dryer of Lemon Slices for Controlling Drying Temperature , 2020, Energies.

[24]  M. Keane,et al.  Climate Change and U.S. Agriculture: Accounting for Multi-dimensional Slope Heterogeneity in Production Functions , 2020 .

[25]  Laurens Klerkx,et al.  A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda , 2019, NJAS - Wageningen Journal of Life Sciences.

[26]  Shuai Luo,et al.  Model selection for accurate daily global solar radiation prediction in China , 2019, Journal of Cleaner Production.

[27]  A. Prasetyaningrum,et al.  Seaweed Drying Process Using Tray Dryer with Dehumidified Air System to Increase Efficiency of Energy and Quality Product , 2019, IOP Conference Series: Earth and Environmental Science.

[28]  D. Rose,et al.  Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming , 2018, Front. Sustain. Food Syst..

[29]  E. Almetwally,et al.  • COMPARISON BETWEEN M-ESTIMATION, S-ESTIMATION, AND MM ESTIMATION METHODS OF ROBUST ESTIMATION WITH APPLICATION AND SIMULATION , 2018 .

[30]  Uzma Aslam,et al.  Policy schemes for the transition to sustainable agriculture—Farmer preferences and spatial heterogeneity in northern Thailand , 2018, Land Use Policy.

[31]  P. Muthukumar,et al.  Drying kinetics and quality analysis of black turmeric (Curcuma caesia) drying in a mixed mode forced convection solar dryer integrated with thermal energy storage , 2018 .

[32]  Shilpa Ankalaki,et al.  Analysis of agriculture data using data mining techniques: application of big data , 2017, Journal of Big Data.

[33]  J. Potting,et al.  Explorative environmental life cycle assessment for system design of seaweed cultivation and drying , 2017 .

[34]  A. Golberg,et al.  Seaweed production: overview of the global state of exploitation, farming and emerging research activity , 2017 .

[35]  M. Muthuvalu,et al.  Cubic spline as a powerful tools for processing experimental drying rate data of seaweed using solar drier , 2017 .

[36]  Heeyoung Kim,et al.  A new metric of absolute percentage error for intermittent demand forecasts , 2016 .

[37]  Kewei Cheng,et al.  Feature Selection , 2016, ACM Comput. Surv..

[38]  Todd A. Gormley,et al.  Common Errors: How to (and Not to) Control for Unobserved Heterogeneity , 2013 .

[39]  R. Ajisaka,et al.  Comparison of epicardial, abdominal and regional fat compartments in response to weight loss. , 2009, Nutrition, metabolism, and cardiovascular diseases : NMCD.

[40]  C. Gross,et al.  Group behavior therapy versus individual behavior therapy for healthy dieting and weight control management in overweight and obese women living in rural community. , 2007, Obesity research & clinical practice.

[41]  S. Rössner,et al.  Results from a randomized controlled trial comparing two low-calorie diet formulae. , 2007, Obesity research & clinical practice.

[42]  Jules Thibault,et al.  Prediction of moisture in cheese of commercial production using neural networks , 2005 .

[43]  Andrew J Vickers,et al.  The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study , 2001, BMC medical research methodology.

[44]  L Kaiser,et al.  Adjusting for baseline: change or percentage change? , 1989, Statistics in medicine.

[45]  L. Törnqvist,et al.  How Should Relative Changes be Measured , 1985 .

[46]  Erkata Yandri,et al.  Comparison between Natural and Cabinet Drying on Weight Loss of Seaweed Euchuema cottonii Weber-van Bosse , 2021 .

[47]  Abhinav Sharma,et al.  Machine Learning Applications for Precision Agriculture: A Comprehensive Review , 2021, IEEE Access.

[48]  Adel Khelifi,et al.  Smart Farming in Europe , 2021, Comput. Sci. Rev..

[49]  Majid Khan Majahar Ali,et al.  Efficient Model Selection of Collector Efficiency in Solar Dryer using Hybrid of LASSO and Robust Regression , 2020 .

[50]  Chelsi Gupta,et al.  Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples , 2019 .

[51]  S. Suherman,et al.  Comparison Drying Behavior of Seaweed in Solar, Sun and Oven Tray Dryers , 2018 .

[52]  M. Muthuvalu,et al.  Post-Harvest Handling of Eucheumatoid Seaweeds , 2017 .

[53]  Lei Yu,et al.  Stable feature selection: theory and algorithms , 2012 .

[54]  Ö. G. Alma.  Comparison of Robust Regression Methods in Linear Regression , 2011 .