113 research outputs found
Monotonicity of a profile of rankings with ties
International Conference, IPMU (17th. 2018. CĂĄdiz
About identification of features that affect the estimation of citrus harvest
Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth.
Highlights:
Red and near-infrared reflectance in February and December are helpful values to predict orange harvest.
SVM is an efficient method to predict harvest.
A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production.
 Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth.
Highlights:
Red and near-infrared reflectance in February and December are helpful values to predict orange harvest.
SVM is an efficient method to predict harvest.
A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production
Intestinal microbiota and associated inflammation in children with nonIgE mediated cowÂŽs milk protein allergy
Trabajo presentado en el ESPGHAN 50th Annual Meeting, celebrado en Praga (RepĂșblica Checa) del 10 al 13 de mayo de 2017Peer reviewe
Reply: âLetter to the editor Re: Diaz M., et al. Nutrients 2018, 10, 1481â
The objective of this letter of reply is to provide answers to the doubts and critical issues that Martín Martinez and López Liñan [...
- âŠ