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

Safari, Hanieh; Fricke, Thomas; Reddersen, Björn; Möckel, Thomas; Wachendorf, M.

doi:10.5194/jsss-5-301-2016

Cited by 15 publications

(14 citation statements)

References 39 publications

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“…These results paralleled with the results from previous studies that come from the same geographical region [15,18]. The study from Safari et al [32] obtained linear regression models with NDSI with R 2 of 0.58 and 0.49 for CP and ADF, respectively. Moreover, the results from Biewer et al [15] reported R 2 values of 0.33 and 0.13 R 2 for CP and ADF models with SR, respectively.…”

Section: Discussionsupporting

confidence: 88%

“…The most frequently applied statistical modelling method is the linear regression (simple linear, step-wise linear) with selected highly correlated spectral features, such as wavebands [20,27], normalised difference spectral indices (NDSIs) [18,24,28], spectral ratios (SRs) [15,29], and other well-known vegetation indices (e.g., NDVI, SAVI, NDRE) [17,23]. Predictive modelling (also known as machine learning) algorithms, such as partial least squares regression (PLSR) [16,27,[30][31][32], random forest regression (RFR) [24,33,34], and artificial neural network [20,21,35], were employed to estimate forage quality parameters using highly correlated spectral reflectance data. Predictive modelling algorithms frequently enhanced the predictive capability compared with the simpler linear regression models [24,34].…”

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confidence: 99%

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Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

et al. 2020

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The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models for multiple grassland types based on an unmanned aerial vehicle (UAV)-borne imaging spectroscopy and (ii) to generate forage quality distribution maps using the best models obtained. The study examined data from eight grasslands in northern Hesse, Germany, which largely differed in terms of vegetation type and cutting regime. The UAV with a hyperspectral camera on board was utilised to acquire spectral images from the grasslands, and crude protein (CP) and acid detergent fibre (ADF) concentration of the forage was assessed at each cut. Five predictive modelling regression algorithms were applied to develop quality estimation models. Further, grassland forage quality distribution maps were created using the best models developed. The normalised spectral reflectance data showed the strongest relationship with both CP and ADF concentration. From all predictive algorithms, support vector regression provided the highest precision and accuracy for CP estimation (median normalised root mean square error prediction (nRMSEp) = 10.6%), while cubist regression model proved best for ADF estimation (median nRMSEp = 13.4%). The maps generated for both CP and ADF showed a distinct spatial variation in forage quality values for the different grasslands and cutting regimes. Overall, the results disclose that UAV-borne imaging spectroscopy, in combination with predictive modelling, provides a promising tool for accurate forage quality estimation of multiple grasslands.

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Section: Discussionsupporting

confidence: 88%

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confidence: 99%

Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

et al. 2020

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“…Furthermore, the results indicate that for a successful estimation of biomass, height information alone is not enough. Evolving sensor fusion approaches might improve the model prediction performance, as it has been shown for example for grassland biomass [42,43].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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“…Alternative devices to plate meter readings, using ultrasonic scanning, that can be towed behind an all-terrain vehicle have been developed (Fricke & Wachendorf, 2013), but accuracy is lower in a moving sensor than for static assessments (Safari, Fricke, Reddersen, Mockel, & Wachendorf, 2016). In future, measurements of herbage mass could be automated using robotically controlled mini-tractors (Yule, 2017) or by using electromagnetic reflectance at different wavelengths to develop vegetative indices to predict forage characteristics (Perez-Sanz, Navarro, & Egea-Cortines, 2017).…”

Section: Grazing Managementmentioning

confidence: 99%

“…In future, measurements of herbage mass could be automated using robotically controlled mini-tractors (Yule, 2017) or by using electromagnetic reflectance at different wavelengths to develop vegetative indices to predict forage characteristics (Perez-Sanz, Navarro, & Egea-Cortines, 2017). However, more improvement would be required in these technological devices since results of an ultrasonic distance sensor assessed by Fricke, Richter, and Wachendorf (2011) were promising but could not exceed prediction accuracies ranging between R 2 = .80 and .82 in legume-grass mixtures (Fricke et al, 2011), and the accuracy decreases (from R 2 = .73 to .52) as the herbage mass increases (Safari et al, 2016).…”

Section: Grazing Managementmentioning

confidence: 99%

Some challenges and opportunities for grazing dairy cows on temperate pastures

Wilkinson

Lee

Rivero

et al. 2019

Grass and Forage Science

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Grazing plays an important role in milk production in most regions of the world. In this review, some challenges to the grazing cow are discussed together with opportunities for future improvement. We focus on daily feed intake, efficiency of pasture utilization, output of milk per head, environmental impact of grazing and the nutritional quality to humans of milk produced from dairy cows in contrasting production systems. Challenges are discussed in the context of a trend towards increased size of individual herds and include limited and variable levels of daily herbage consumption, lower levels of milk output per cow, excessive excretion of nitrogenous compounds and requirements for minimal periods of grazing regardless of production system.A major challenge is to engage more farmers in making appropriate adjustments to their grazing management. In relation to product quality, the main challenge is to demonstrate enhanced nutritional/processing benefits of milk from grazed cows.Opportunities include more accurate diet formulations, supplementation of grazed pasture to match macro-and micronutrient supply with animal requirement and plant breeding. The application of robotics and artificial intelligence to pasture management will assist in matching daily supply to animal requirement. Wider consumer recognition of the perceived enhanced nutritional value of milk from grazed cows, together with greater appreciation of the animal health, welfare and behavioural benefits of grazing should contribute to the future sustainability of demand for milk from dairy cows on pasture. K E Y W O R D Sgrazed pasture, grazing management, herbage intake, milk production, milk quality, nitrogen use efficiency Highlights• Grazing plays a central role in the nutrition of dairy cows in many regions of the world.• Challenges to efficient management of grazed pastures include variable herbage supply and low herbage intake, which limit milk output per head.• Opportunities for improved efficiency include more accurate diet formulation, breeding superior plant cultivars to improve ruminal efficiency and enhance product nutritional quality, application of robotics, sensors and artificial intelligence to improve grazing management and pasture utilization, and wider consumer recognition of the perceived enhanced value of milk from grazed cows.TA B L E 1 Average size of dairy herd, average annual milk yield per cow and average annual output of milk per herd in New Zealand,

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

Cited by 15 publications

References 39 publications

Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Some challenges and opportunities for grazing dairy cows on temperate pastures

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