PSX-23 Use of unmanned aerial vehicle technologies to assess nutrient landscape of cattle pens managed with energy dense vs. low energy feeding programs.
Year: 2025
Type: article
Abstract: An experiment was conducted to determine if feedlot pen conditions can be detected using unmanned aerial vehicle (UAV) technologies. Steers fed an energy-dense limit-fed diet or low-energy ad-libitum-fed diet were used to create a gradient in pen nutrient landscapes. The objectives were (1) characterize direct measurements to describe the landscape of cattle pens managed with energy-dense or low-energy feeding programs, and (2) evaluate the association of thermal images taken using UAV technologies with the nutrient landscape of feedlot pens. Dirt floor feedlot pens (n=6) (9.1 m x 15.2 m) stocked with 14 dairy-beef crossbred steers (avg. BW = 453.6 kg) at the Kansas State University Stocker Unit (Manhattan, KS) were used in the study. Two feeding programs were applied to the pens: 1) energy-dense 64 NEg limit-fed ration (n=3) and 2) low-energy 50 NEg ad libitum fed ration (n=3). Prior to data collection, pens were gridded into 15 quadrants (3 m x 3 m). Pen quadrants were further classified into pen regions. During the first daily feeding, a UAV (DJI M300) equipped with a thermal sensor (H20T) was flown over each pen (30 m altitude) to capture images. Images were processed using Pix4D, visualized using ArcGIS, and Photoshop was used to obtain color values (luminance) for each quadrant using 3 random sampling points. Within each pen, 3 pen floor grab samples (250 g) and 3 direct temperature measurements were taken from random locations within each quadrant and analyzed for moisture, ash, NDF, and ADF. Linear mixed-effects models were used to evaluate the effect of feeding program, pen region, and feeding program by pen region for each direct measurement (moisture, ash, moisture:ash, NDF, ADF, direct temperature, luminance). Differences in direct measurements across physical locations within feedlot pen by feeding program were identified for moisture (P< 0.01), moisture:ash (P< 0.01), direct temperature (P< 0.01), luminance (P< 0.01), ADF (P< 0.01), and NDF (P< 0.01). A trend (P=0.10) for an association between pen region and feeding program for ash was observed. Direct measurements with identified differences (P< 0.05) across the pen regions by feeding program were then tested for association by pen region to luminance using linear mixed-effects models. Results indicate a significant relationship between luminance and all direct measurement variables (P< 0.05). Lin’s concordance correlation coefficient was used to understand precision and accuracy between direct measurement variables and luminance. Moisture demonstrates good accuracy and precision (CCC=0.82) when predicted from luminance values. Results indicate moisture can be used to characterize pen landscape from UAV thermal images.
Source: Journal of Animal Science
Institution: Kansas State University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4414830729
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