@inproceedings{70cfa0715d004c598f0336662420e0dd,
title = "Aerial monitoring of rice crop variables using an UAV robotic system",
abstract = "This paper presents the integration of an UAV for the autonomous monitoring of rice crops. The system integrates image processing and machine learning algorithms to analyze multispectral aerial imagery. Our approach calculates 8 vegetation indices from the images at each stage of rice growth: vegetative, reproductive and ripening. Multivariable regressions and artificial neural networks have been implemented to model the relationship of these vegetation indices against two crop variables: biomass accumulation and leaf nitrogen concentration. Comprehensive experimental tests have been conducted to validate the setup. The results indicate that our system is capable of estimating biomass and nitrogen with an average correlation of 80% and 78% respectively.",
keywords = "Image Processing, Machine Learning, Multispectral Imagery, Precision Agriculture, UAV, Vegetative Indices",
author = "C. Devia and J. Rojas and E. Petro and C. Martinez and I. Mondragon and D. Patino and C. Rebolledo and J. Colorado",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved; 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 ; Conference date: 29-07-2019 Through 31-07-2019",
year = "2019",
doi = "10.5220/0007909900970103",
language = "English",
series = "ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics",
publisher = "SciTePress",
pages = "97--103",
editor = "Oleg Gusikhin and Kurosh Madani and Janan Zaytoon",
booktitle = "ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics",
}