agronomy Article QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps Lia Duarte 1,2, * , Ana Cláudia Teodoro 1,2 , Joaquim J. Sousa 3,4 and LuísPádua 3,4   Citation: Duarte, L.; Teodoro, A.C.; Sousa, J.J.; Pádua, L. QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps. Agronomy 2021, 11, 952. https:// doi.org/10.3390/agronomy11050952 Academic Editors: Jitka Kumhálová, Jan Lukáš, Pavel Hamouz and Jose Antonio Dominguez-Gómez Received: 12 April 2021 Accepted: 8 May 2021 Published: 11 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Institute of Earth Sciences, FCUP Pole, Rua do Campo Alegre, 4169-007 Porto, Portugal; amteodor@fc.up.pt 2 Department of Geosciences, Environment and Spatial Planning, FCUP, 4169-007 Porto, Portugal 3 Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal; jjsousa@utad.pt (J.J.S.); luispadua@utad.pt (L.P.) 4 Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), 4200-465 Porto, Portugal * Correspondence: liaduarte@fc.up.pt; Tel.: +351-220-402-473 Abstract: In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegeta- tion Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distri- bution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour. Keywords: precision agriculture; multispectral imagery; variable rate; precision viticulture; GIS 1. Introduction According to the definition adopted by the International Society of Precision Agricul- ture, Precision Agriculture (PA) can be defined as the “management strategy that gathers, processes and analyses temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for im- proved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production” [1]. Sassu et al. [2] argue that PA is related with the use of technol- ogy to manage the spatial and temporal variability associated with agricultural production, so that it can improve the crop performance, bringing economic benefits and environmen- tal quality with the correct use of pollutants. Regardless of the sources used to establish PA, they all include several innovative technologies, such as the use of data acquired by Unmanned Aerial Vehicles (UAVs), satellite imagery data, the use of Geographical Information Systems (GIS) technologies, nutrient management field mapping, Internet of Things (IoT) sensors, and data processing techniques such as Machine Learning and Deep Learning, among others, allowing experts to use specific tools to optimize agriculture production [2–4]. Rmote sensing imagery, such as Sentinel-2 images or Landsat, combined Agronomy 2021, 11, 952. https://doi.org/10.3390/agronomy11050952 https://www.mdpi.com/journal/agronomy