DOI：10.31220/agriRxiv.2021.00092

Boosting algorithms for prediction in agriculture: an application of feature importance and feature selection boosting algorithms for prediction crop damage.

•  

  Abstract: Abstract The Agriculture sector has created and collected large amounts of data. It can be gathered, stored, and analyzed to assist in decision making generating competitive value, and the use of Machine Learning techniques has been very effective for this market. In this work, a Machine Learning study was carried out using supervised classification models based on boosting to predict disease in a crop, thus identifying the model with the best areas under curve metrics. Light Gradient Boosting Machine, CatBoost Classifier, Extreme Gradient, Gradient Boosting Classifier, Adaboost models were used to qualify the crop as healthy or sick. One can see that the LightGBM algorithm provided a better fit to the data with an area under the curve of 0.76 under the use of BORUTA variable selection.

  Key words: prediction; use of; crop; boosting algorithms; data; based o

  提交时间：2021-01-01

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
• html

• 图表

• Rohit Raj Jha, Gopal Prasad Khanal, Kavita Isvara. Factors affecting crop field use by Blackbuck in Krishnasaar Conservation Area, Nepal.. 2021. doi: 10.31220/agriRxiv.2021.00098

  Anna Rini, N. Hemalatha, Raji Sukuma. Decision tree classification of digital soil, weather, crop mapping and yield prediction using linear regression with region influences.. 2021. doi: 10.31220/agriRxiv.2021.00072

  Akhil Wilson, N. Hemalatha, Raji Sukuma. Computational prediction model for pepper yield prediction using support vector regression.. 2021. doi: 10.31220/agriRxiv.2021.00069

  Munir Zia, A. Lodhi, Muhammad Zahid Aziz, Aftab Naseem, Waqar Ahmad, R. Murray Lark, Michael J. Watts, Martin R. Broadley, E.L. Ande. Soil fertility mapping and agronomic advice at the regional scale using private sector data.. 2021. doi: 10.31220/agriRxiv.2021.00113

  Samuel V.J. Robinson, Lan H. Nguyen, Paul Galper. Livin' on the edge: precision yield data shows evidence of ecosystem services from field boundaries.. 2021. doi: 10.31220/agriRxiv.2021.00089

  Yusuke Toda, Goshi Sasaki, Yoshihiro Omori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Masayuki Tsuda, Hiromi Kajiya‐Kanegae, Raúl López-Lozano, Hisashi Tsujimoto, Akito Kaga, Mikio Nakazono, Toru Fujiwara, Frédéric Baret, Hiroyoshi Iwat. Genomic prediction of green fraction dynamics in soybean using UAV observations.. 2021. doi: 10.31220/agriRxiv.2021.00097

  Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. doi: 10.31220/agriRxiv.2021.00070

  Adam Sparks, Emerson M. Del Ponte, Kaique S. Alves, Zachary Foster, Niklaus J. Grünwal. Openness and Computational Reproducibility in Plant Pathology: Where we Stand and a Way Forward. 2021. doi: 10.31220/agriRxiv.2021.00082

  Lucius Tamm, Barbara Thuerig, Stoilko Apostolov, Hugh Blogg, Esmeralda Borgo, Paola E. Corneo, Susanne Fittje, Michelangelo de Palma, Ádám Donkó, Catherine Experton, E. Marin, Ángela Morell Pérez, I. Pertot, Anton M. H. Rasmussen, Håvard Steinshamn, Airi Vetemaa, Helga Willer, Joëlle Herforth-Rahm. Copper use in organic agriculture in twelve European countries.. 2021. doi: 10.31220/agriRxiv.2021.00108

  K. V. Daniel, Mark A. Bradford, Emma Fuller, Emily E. Oldfield, Stephen A. Woo. Soil organic matter effects on US maize production and crop insurance payouts under drought.. 2020. doi: 10.31220/agriRxiv.2020.00018

• 序号	提交日期	编号	操作
  1	2021-01-01	10.31220/agriRxiv.2021.00092V1	下载

• 公开评论  匿名评论  仅发给作者 提交评论