Fast Pasture Classification Method using Ground-based Camera and the Modified Green Red Vegetation Index (MGRVI)
Boris Evstatiev,Nikolay Valov,T. Zhelyazkova,Mariya Gerdzhikova,M. Todorova,Neli Grozeva,Atanas Sevov,G. S. Stanchev +7 more
TL;DR: In this article , the authors presented a method for fast approximation of pastures' biomass based on photos made by stationary or mobile ground-based visual spectrum camera, using raster analysis, based on the MGRVI index, in order to classify the pasture into two categories: ''grazed'' and ''ungrazed''.
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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—Pasture biomass; MGRVI; ground-based camera; classification
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References
Remote sensing for agricultural applications: A meta-review
TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
1.2K
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Juliane Bendig,Kang Yu,Helge Aasen,Andreas Bolten,Simon Bennertz,Janis Broscheit,Martin L. Gnyp,Martin L. Gnyp,Martin L. Gnyp,Georg Bareth,Georg Bareth +10 more
TL;DR: Combining VIs and plant height information by using multiple linear regression or multiple non-linear regression models performed better than the VIs alone, and it was found that the visible band VIs have potential for biomass prediction prior to heading stage.
1K
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TL;DR: The most common applications, the types of UAVs exploited, and the most popular processing methods of aerial imagery are discussed, to discuss the outcomes of each method and the potential applications of each one in the farming operations.
844
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490
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