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.

Boris Evstatiev, Tsvetelina Mladenova, Nikolay Valov, Tsenka Zhelyazkova, Mariya Gerdzhikova, Mima Todorova, Neli Grozeva, Atanas Sevov and Georgi Stanchev, “Fast Pasture Classification Method using Ground-based Camera and the Modified Green Red Vegetation Index (MGRVI)” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140605

@article{Evstatiev2023,
title = {Fast Pasture Classification Method using Ground-based Camera and the Modified Green Red Vegetation Index (MGRVI)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140605},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140605},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Boris Evstatiev and Tsvetelina Mladenova and Nikolay Valov and Tsenka Zhelyazkova and Mariya Gerdzhikova and Mima Todorova and Neli Grozeva and Atanas Sevov and Georgi Stanchev}
}