Multispectral images, IoT data to estimate foliar nitrogen in Pineapple crops

Published: 28 October 2023| Version 1 | DOI: 10.17632/94pxtywr6x.1
Jorge Enrique Chaparro Mesa,


This repository hosts a portion of the data used in the article titled "Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms." Within this repository, the following elements pertain to the initial sampling date of June 30, 2022: Multispectral Images: Images captured on this date, organized by Blocks and treatments. Ecological Factors Data: Information detailing ecological factors recorded on this date. Vegetation Index Statistics Data Set: A dataset encompassing statistical measures of vegetation indices. Python Notebook: A Python Notebook file containing codes and explanations utilized for radiometric calibration, image alignment, vegetation index computation, and the requisite data for Machine Learning model implementation, inclusive of the corresponding results. These resources constitute an integral part of the research, facilitating access to valuable data and information employed in the study. It should be noted that the information presented here is exclusively intended for academic purposes and should not be employed for any other purpose.



Universidad de Antioquia, Fundacion Universitaria Internacional del Tropico Americano, Universitat Autonoma de Barcelona


Machine Learning Algorithm, Precision Agriculture, Tropical Crops


Ministerio de Ciencia, Tecnología e Innovación