Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage-Reference-Cited by-同舟云学术

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Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Published:2024-09 Issue: Volume:224 Page:109221
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Mwiti Frankline^ORCID,Gitau Ayub,Mbuge Duncan

Publisher

Elsevier BV

Reference40 articles.

1. Prediction of draft force of a chisel cultivator using artificial neural networks and its comparison with regression model;Abbaspour-Gilandeh;Agronomy,2020

2. Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil;Abdullah;Sci. Rep.,2024

3. Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods;Alzabeebee;Transp. Geotech.,2020

4. ASABE, 2011. ASAE D497.7 MAR2011 Agricultural machinery management data. ASABE standards 2011. St. Joseph, MI.

5. Application of computational intelligence methods in agricultural soil–machine interaction: A review;Badgujar;Agriculture,2023

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