Harvesters’ productivity prediction in Brazilian Eucalyptus plantations: development of a model from machine learning

This publication has 82 references indexed in Scilit:

• Understanding the influence of tree size distribution on the CTL harvesting productivity of two different size harvesting machines
  European Journal of Forest Research, 2024
• The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.)
  Genetic Resources and Crop Evolution, 2021
• Using harvester data from on-board computers: a review of key findings, opportunities and challenges
  European Journal of Forest Research, 2020
• Separating the impact of work environment and machine operation on harvester performance
  European Journal of Forest Research, 2020
• A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations
  Journal of Forestry Research, 2019
• Machine learning in materials science
  InfoMat, 2019
• In-stand debarking with the use of modified harvesting heads: a potential solution for key challenges in European forestry
  European Journal of Forest Research, 2019
• A comparison between two alternative harvesting systems in the thinning of fast-growing pine plantations under the conditions of low labour cost
  European Journal of Forest Research, 2018
• Correlation and variable importance in random forests
  Statistics and Computing, 2016
• Determining the shape of the productivity function for mechanized felling and felling-processing
  Journal of Forest Research, 2011