scholarly journals A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures

2016 ◽  
Vol 13 (16) ◽  
pp. 4673-4695 ◽  
Author(s):  
Rebecca N. Handcock ◽  
D. L. Gobbett ◽  
Luciano A. González ◽  
Greg J. Bishop-Hurley ◽  
Sharon L. McGavin
Keyword(s):  
Vegetation Indices ◽  
Digital Camera ◽  
Ground Cover ◽  
Pilot Project ◽  
Three Dimensions ◽  
Sensor Data ◽  
Digital Cameras ◽  
Wet Season ◽  
Tropical Pastures ◽  
Green Vegetation

Abstract. Timely and accurate monitoring of pasture biomass and ground cover is necessary in livestock production systems to ensure productive and sustainable management. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since data can be returned in near real time. Proximal sensors have the potential for deployment on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. There are unresolved challenges in gathering reliable sensor data and in calibrating raw sensor data to values such as pasture biomass or vegetation ground cover, which allow meaningful interpretation of sensor data by livestock producers. Our goal was to assess whether a combination of proximal sensors could be reliably deployed to monitor tropical pasture status in an operational beef production system, as a precursor to designing a full sensor deployment. We use this pilot project to (1) illustrate practical issues around sensor deployment, (2) develop the methods necessary for the quality control of the sensor data, and (3) assess the strength of the relationships between vegetation indices derived from the proximal sensors and field observations across the wet and dry seasons. Proximal sensors were deployed at two sites in a tropical pasture on a beef production property near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multispectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each of which were operated over 18 months. Raw data from each sensor was processed to calculate multispectral vegetation indices. The data capture from the digital cameras was more reliable than the multispectral sensors, which had up to 67 % of data discarded after data cleaning and quality control for technical issues related to the sensor design, as well as environmental issues such as water incursion and insect infestations. We recommend having a system with both sensor types to aid in data interpretation and troubleshooting technical issues. Non-destructive observations of pasture characteristics, including above-ground standing biomass and fractional ground cover, were made every 2 weeks. This simplified data collection was designed for multiple years of sampling at the remote site, but had the disadvantage of high measurement uncertainty. A bootstrapping method was used to explore the strength of the relationships between sensor and pasture observations. Due to the uncertainty in the field observations, the relationships between sensor and field data are not confirmational and should be used only to inform the design of future work. We found the strongest relationships occurred during the wet season period of maximum pasture growth (January to April), with generally poor relationships outside of this period. Strong relationships were found with multispectral indices that were sensitive to the green and dry components of the vegetation, such as those containing the band in the lower shortwave infrared (SWIR) region of the electromagnetic spectrum. During the wet season the bias-adjusted bootstrap point estimate of the R2 between above-ground biomass and the normalized ratio between the SWIR and red bands (NVI-SR) was 0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of green vegetation observed in three dimensions and a simple ratio between the near infrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00). Relationships between field data and the vegetation index derived from the digital camera images were generally weaker than from the multispectral sensor data, except for green vegetation observations in two and three dimensions. Our successful pilot of multiple proximal sensors supports the design of future deployments in tropical pastures and their potential for operational use. The stringent rules we developed for data cleaning can be more broadly applied to other sensor projects to ensure quality data. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely on-farm decision-making.

scholarly journals Combining multi-spectral proximal sensors and digital cameras for monitoring grazed tropical pastures

2015 ◽  
Vol 12 (21) ◽  
pp. 18007-18051
Author(s):  
R. N. Handcock ◽  
D. L. Gobbett ◽  
L. A. González ◽  
G. J. Bishop-Hurley ◽  
S. L. McGavin
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Digital Camera ◽  
Ground Cover ◽  
Sensor Data ◽  
Weather Data ◽  
Research Station ◽  
Digital Cameras ◽  
Tropical Pastures ◽  
Standing Biomass

Abstract. Timely and accurate monitoring of pasture biomass and ground-cover is necessary in livestock production systems to ensure productive and sustainable management of forage for livestock. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since such sensors can return data in near real-time, and have the potential to be deployed on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. However, there are unresolved challenges in developing calibrations to convert raw sensor data to quantitative biophysical values, such as pasture biomass or vegetation ground-cover, to allow meaningful interpretation of sensor data by livestock producers. We assessed the use of multiple proximal sensors for monitoring tropical pastures with a pilot deployment of sensors at two sites on Lansdown Research Station near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multi-spectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each operated over 18 months. Raw data from each sensor were processed to calculate a number of multispectral vegetation indices. Visual observations of pasture characteristics, including above-ground standing biomass and ground cover, were made every 2 weeks. A methodology was developed to manage the sensor deployment and the quality control of the data collected. The data capture from the digital cameras was more reliable than the multi-spectral sensors, which had up to 63 % of data discarded after data cleaning and quality control. We found a strong relationship between sensor and pasture measurements during the wet season period of maximum pasture growth (January to April), especially when data from the multi-spectral sensors were combined with weather data. RatioNS34 (a simple band ratio between the near infrared (NIR) and lower shortwave infrared (SWIR) bands) and rainfall since 1 September explained 91 % of the variation in above-ground standing biomass (RSE = 593 kg DM ha−1, p < 0.01). RatioNS34 together with rainfall explained 95 % of the variation in the percentage of green vegetation observed in 2-dimensions (%Green2D) (RSE = 6 %, p < 0.01). The Green Leaf Algorithm index derived from the digital camera images and the rainfall accumulated since the 1 September explained 91 % of the variation in %Green2D (RSE = 9 %, p < 0.01, df = 20), but had a poor relationship with biomass. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely decision-making on-farm.

scholarly journals Digital models based on images taken with handheld cameras – examples on land, from the sea and on ice

1969 ◽  
pp. 73-76
Author(s):  
Erik Vest Sørensen ◽  
Morten Bjerager ◽  
Michele Citterio
Keyword(s):  
Imaging Techniques ◽  
Digital Camera ◽  
Point Clouds ◽  
Three Dimensions ◽  
Digital Cameras ◽  
Ice Cap ◽  
North East ◽  
Recent Developments ◽  
Digital Outcrop ◽  
Micro Topography

Geological outcrops can be comfortably modelled in three dimensions in the offi ce using images from a handheld digital camera. Recent developments within the imaging techniques of Structure from Motion (Lowe 2004; Snavely et al. 2008; Fonstad et al. 2013) and photogrammetry (Hirschmüller 2005; James & Robson 2012; Favalli et al. 2012) have made it easier and cheaper to construct so-called digital outcrop models using stereoscopic images from standard digital cameras. Th e digital outcrop model (Bellian et al. 2005) is a 3D representation of the outcrop surface and is oft en displayed in the form of a polygon mesh or a point cloud. In this paper we present three examples of such point clouds from images obtained with a handheld digital camera. Th e examples illustrate how outcrop topography or digital outcrop models can be constructed at diff erent scales, with diff erent accessibility and operational platforms. Two examples illustrate outcrop scales of metres to kilometres, with images obtained by walking along excavated exposures in the Faxe limestone quarry and from a boat sailing past the coastal cliff of Stevns Klint. Th e third example illustrates detailed micro-topography of ice and snow surfaces where the images were obtained from a snowmobile on an ice cap in A.P. Olsen Land, North-East Greenland.

Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

2019 ◽  
Vol 57 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Jaebeom Kim ◽  
Sinkyu Kang ◽  
Bumsuk Seo ◽  
Amratuvshin Narantsetseg ◽  
Youngji Han
Keyword(s):  
Vegetation Cover ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Green Vegetation

scholarly journals Optical Inspection Systems for Axisymmetric Parts with Spatial 2D Resolution

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1218
Author(s):  
Aleksandr Kulchitskiy
Keyword(s):  
Control System ◽  
Optical System ◽  
Measurement Accuracy ◽  
Experimental Studies ◽  
Digital Camera ◽  
Digital Cameras ◽  
Bodies Of Revolution ◽  
Optical Inspection ◽  
Inspection Systems ◽  
Projection Error

The article proposes a solution to the problem of increasing the accuracy of determining the main shaping dimensions of axisymmetric parts through a control system that implements the optical method of spatial resolution. The influence of the projection error of a passive optical system for controlling the geometric parameters of bodies of revolution from the image of its sections, obtained by a digital camera with non-telecentric optics, on the measurement accuracy is shown. Analytical dependencies are derived that describe the features of the transmission of measuring information of a system with non-telecentric optics in order to estimate the projection error. On the basis of the obtained dependences, a method for compensating the projection error of the systems for controlling the geometry of the main shaping surfaces of bodies of revolution has been developed, which makes it possible to increase the accuracy of determining dimensions when using digital cameras with a resolution of 5 megapixels or more, equipped with short-focus lenses. The possibility of implementing the proposed technique is confirmed by the results of experimental studies.

scholarly journals Bionic Birdlike Imaging Using a Multi-Hyperuniform LED Array

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4084
Author(s):  
Xin-Yu Zhao ◽  
Li-Jing Li ◽  
Lei Cao ◽  
Ming-Jie Sun
Keyword(s):  
Imaging System ◽  
Color Image ◽  
Digital Camera ◽  
Frame Rate ◽  
Digital Cameras ◽  
Structured Illumination ◽  
Pixel Resolution ◽  
Led Array ◽  
Frequency Aliasing ◽  
Imaging Sensors

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.

Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras

2021 ◽  
Vol 2021 (29) ◽  
pp. 1-6
Author(s):  
Yuteng Zhu ◽  
Graham D. Finlayson
Keyword(s):  
White Light ◽  
Digital Camera ◽  
Color Filter ◽  
Digital Cameras ◽  
Spectral Transmittance ◽  
Optimal Filters ◽  
Prior Art ◽  
Average Transmittance ◽  
Tristimulus Values ◽  
Correction Problem

Previously improved color accuracy of a given digital camera was achieved by carefully designing the spectral transmittance of a color filter to be placed in front of the camera. Specifically, the filter is designed in a way that the spectral sensitivities of the camera after filtering are approximately linearly related to the color matching functions (or tristimulus values) of the human visual system. To avoid filters that absorbed too much light, the optimization could incorporate a minimum per wavelength transmittance constraint. In this paper, we change the optimization so that the overall filter transmittance is bounded, i.e. we solve for the filter that (for a uniform white light) transmits (say) 50% of the light. Experiments demonstrate that these filters continue to solve the color correction problem (they make cameras much more colorimetric). Significantly, the optimal filters by restraining the average transmittance can deliver a further 10% improvement in terms of color accuracy compared to the prior art of bounding the low transmittance.

Part Monitoring and Quality Assessment of Conformal Additive Manufacturing Using Image Reconstruction

2018 ◽  
Author(s):  
Michael D. Kutzer ◽  
Levi D. DeVries ◽  
Cooper D. Blas
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Digital Camera ◽  
Image Data ◽  
Digital Cameras ◽  
Public And Private ◽  
The Public ◽  
Fused Deposition ◽  
Complex Part ◽  
Deposition Modeling

Additive manufacturing (AM) technologies have become almost universal in concept development, prototyping, and education. Advances in materials and methods continue to extend this technology to small batch and complex part manufacturing for the public and private sectors. Despite the growing popularity of digital cameras in AM systems, use of image data for part monitoring is largely unexplored. This paper presents a new method for estimating the 3D internal structure of fused deposition modeling (FDM) processes using image data from a single digital camera. Relative transformations are established using motion capture, and the 3D model is created using knowledge of the deposition path coupled with assumptions about the deposition cross-section. Results show that part geometry can be estimated and visualized using the methods presented in this work.

Returns to search when consumers use and do not use recommendation agents

2017 ◽  
Vol 29 (4) ◽  
pp. 813-836 ◽  
Author(s):  
Satish Sasalu Maheswarappa ◽  
Bharadhwaj Sivakumaran ◽  
Arun G. Kumar
Keyword(s):  
Digital Camera ◽  
Decision Quality ◽  
Digital Cameras ◽  
Content Type ◽  
Subjective Knowledge ◽  
Decision Confidence ◽  
Optimum Decision ◽  
Decision Satisfaction ◽  
Recommendation Agents ◽  
Quality Decision

Purpose The purpose of this paper is to investigate returns to search (getting a better product and/or a lower price as a result of search) when consumers use/do not use recommendation agents (RAs). Specifically, it studies the effect of RAs/no RAs on decision quality, decision confidence and decision satisfaction taking into account subjective knowledge (SK) and involvement. Design/methodology/approach This paper employed two between-subjects factorial experimental designs with subjects searching for digital cameras in a simulated online digital camera store. The experiment was conducted with graduate students in Chennai, Bengaluru and Mysore in India. Findings Results of two online experiments showed that when consumers used RAs, low search led to better decision quality, whereas when consumers did not use RAs, medium search led to optimum decision quality. When consumers use RAs, SK had a U-shaped influence on the decision quality indicating that decision quality was the lowest for those with medium SK. When consumers did not use RAs, the effect of SK on decision quality was an inverted U-shape, indicating optimum decision quality for medium SK consumers. When consumers did not use RAs, subjects with high involvement made better choices, whereas when consumers used RAs, low involvement subjects made better choices. However, subjects who searched more had higher decision confidence and decision satisfaction even if their choices were not better. Originality/value The effect of RA vs no RA in conjunction with relevant consumer characteristics influencing decision quality of the consumer is demonstrated in this study. The findings have important managerial, consumer and theoretical contributions to make.

The use of RGB cameras in defining crop development in legumes

2017 ◽  
Vol 8 (2) ◽  
pp. 224-228 ◽  
Author(s):  
I. Travlos ◽  
A. Mikroulis ◽  
E. Anastasiou ◽  
S. Fountas ◽  
D. Bilalis ◽  
...  
Keyword(s):  
Human Population ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Vegetation Indices ◽  
Digital Camera ◽  
High Yield ◽  
Legume Crop ◽  
Crop Varieties ◽  
Crop Development ◽  
Crop Parameters

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

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