Estimation of Biomass and Canopy Height in Bermudagrass, Alfalfa, and Wheat Using Ultrasonic, Laser, and Spectral Sensors

Abstract

Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha−1, respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha−1) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha−1). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation.

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

“…This outcome can be explained by the higher difficulty involved in the detection of less tall objects and their modelling due to (a) their closer distance to the soil that increases the difficulty for the SfM software and (b) the taller surrounding grass, especially at the end of the growth period that reduces its visibility in the extreme oblique images. To obtain the DTM needed to build the CHM, it is possible to use a flight without vegetation (Bendig et al, ), interpolation of points (manually or automatically) classified as ground using the borders of the plots or visual bare soil areas (Viljanen et al, ; Yue et al, ), to use a ultrasonic or laser scanner (Pittman et al, ) or interpolating X , Y and Z coordinates of the plot corners measured with a GPS (Chang, Jung, Maeda, & Landivar, ). In our case, very good results were obtained using interpolated coordinates measured with the RTK GPS.…”

“…Therefore, to the best of our knowledge, estimation of DMY from canopy height over an entire growing season for perennial ryegrass using high‐resolution UAV‐RGB images has not been reported yet. This is especially challenging as the height of perennial ryegrass canopies are typically lower (0.2 and 0.4 m) than those of other forage crop species such as timothy–meadow fescue mixtures (0.6–0.7 m) (Viljanen et al, ) and bermudagrass/alfalfa mixtures (0.8–1.1 m) (Pittman, Arnall, Interrante, Moffet, & Butler, ).…”

Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system

“…Hence, the application of sensors in grazed pastures is more difficult than in cut grassland, and there are limitations for each specific sensor technique used for the prediction of sward characteristics (Schellberg et al, 2008;Pullanagari et al, 2012). An effective method for in-field estimation of biomass must reach an accuracy comparable to the accepted standard of destructive procedure (clipping and weighing) (Pittman et al, 2015). Using a data combination of conceptually different sensing methods holds promise for providing more accurate property estimates (Adamchuk et al, 2004).…”

“…Farooque et al (2013) developed an integrated automated system comprising an ultrasonic sensor, a digital color camera, a slope sensor and a global positioning system (GPS) to measure plant height, fruit yield, slope and elevation in wild blueberry fields and concluded the developed system was accurate, reliable and efficient to map such characteristics in real-time kinematics (RTK). Pittman et al (2015) examined several types of ground-based mobile-sensing strategies (ultrasonic, laser and spectral sensors) to estimate biomass and canopy height in Bermuda grass, alfalfa and wheat. They suggested that using mobile-sensor-based biomass estimation methods could be an effective alternative to the traditional clipping method for a rapid and accurate in-field biomass estimation.…”

Sensors for measuring plant phenotyping: A review

“…Recently, acoustic spectrum in a wide frequency range were applied, and the quality of the ultrasonic signals are significantly improved [37] , which may promote its application. To improve measurement accuracy, ultrasonic sensors can be also combined with lidars [38] .…”