Fast Pasture Classification Method using Ground-based Camera and the Modified Green Red Vegetation Index (MGRVI)
Authors:
- Boris EvstatievBoris Evstatiev
- Tsvetelina MladenovaTMTsvetelina Mladenova
- Tsenka ZhelyazkovaTZTsenka Zhelyazkova
- Mariya GerdzhikovaMGMariya Gerdzhikova
- Mima TodorovaMTMima Todorova
- Atanas SevovASAtanas Sevov
- Georgi StanchevGSGeorgi Stanchev
Open Access
- 1 January 2023
- journal article
- Published by The Science and Information Organization in International Journal of Advanced Computer Science and Applications
- Vol. 14 (6)
Abstract
The assessment of aboveground biomass is important for achieving rational usage of pasture resources and for maximizing the quantity and quality of milk and meat production. This study presents a method for fast approximation of pastures’ biomass. Unlike most similar studies, which rely on unmanned aerial vehicle and satellite obtained data, this study focuses on photos made by stationary or mobile ground-based visual spectrum camera. The developed methodology uses raster analysis, based on the MGRVI index, in order to classify the pasture into two categories: “grazed” and “ungrazed”. Thereafter, the developed methodology accounts for the perspective in order to obtain the actual area of each class in square meters and in percent. The methodology was applied on an experimental pasture, located near the city of Troyan (Bulgaria). Two images were selected, with the first one representing a mostly ungrazed pasture and the second one – a mostly grazed one. Thereafter the images were analyzed using QGIS 3.0 as well as a specially developed software tool. An important advantage of the proposed methodology is that it does not require expensive equipment and technological knowledge, as it relies on commonly available tools, such as the camera of mobile phones.Keywords
This publication has 28 references indexed in Scilit:
- Long-Term Trends in Vegetation on Bureau of Land Management Rangelands in the Western United StatesRangeland Ecology & Management, 2022
- Considering Spatiotemporal Forage Variability in Rangeland Inventory and MonitoringRangeland Ecology & Management, 2021
- Remote sensing for agricultural applications: A meta-reviewRemote Sensing of Environment, 2020
- Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the worldRemote Sensing of Environment, 2019
- Short-Term Effects on Diversity and Biomass on Grasslands from Artificial Dykes under Grazing and Mowing TreatmentsEnvironmental Conservation, 2018
- Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattleCrop & Pasture Science, 2017
- Modeling grassland aboveground biomass using a pure vegetation indexEcological Indicators, 2016
- Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barleyInternational Journal of Applied Earth Observation and Geoinformation, 2015
- Estimating above-ground biomass on mountain meadows and pastures through remote sensingInternational Journal of Applied Earth Observation and Geoinformation, 2015
- Estimating soil moisture using remote sensing data: A machine learning approachAdvances in Water Resources, 2010