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 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 3D Calibration Test-Field for Digital Cameras Mounted on Unmanned Aerial Systems (UAS)

2018 ◽  
Vol 10 (12) ◽  
pp. 2017 ◽  
Author(s):  
Valeria-Ersilia Oniga ◽  
Norbert Pfeifer ◽  
Ana-Maria Loghin
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Digital Camera ◽  
Field Measurements ◽  
Unmanned Aerial Systems ◽  
Test Field ◽  
Digital Cameras ◽  
Self Calibration ◽  
Technological Developments ◽  
Aerial Systems

Due to the large number of technological developments in recent years, UAS systems are now used for monitoring purposes and in projects with high precision demand, such as 3D model-based creation of dams, reservoirs, historical monuments etc. These unmanned systems are usually equipped with an automatic pilot device and a digital camera (photo/video, multispectral, Near Infrared etc.), of which the lens has distortions; but this can be determined in a calibration process. Currently, a method of “self-calibration” is used for the calibration of the digital cameras mounted on UASs, but, by using the method of calibration based on a 3D calibration object, the accuracy is improved in comparison with other methods. Thus, this paper has the objective of establishing a 3D calibration field for the digital cameras mounted on UASs in terms of accuracy and robustness, being the largest reported publication to date. In order to test the proposed calibration field, a digital camera mounted on a low-cost UAS was calibrated at three different heights: 23 m, 28 m, and 35 m, using two configurations for image acquisition. Then, a comparison was made between the residuals obtained for a number of 100 Check Points (CPs) using self-calibration and test-field calibration, while the number of Ground Control Points (GCPs) variedand the heights were interchanged. Additionally, the parameters where tested on an oblique flight done 2 years before calibration, in manual mode at a medium altitude of 28 m height. For all tests done in the case of the double grid nadiral flight, the parameters calculated with the proposed 3D field improved the results by more than 50% when using the optimum and a large number of GCPs, and in all analyzed cases with 75% to 95% when using a minimum of 3 GCP. In this context, it is necessary to conduct accurate calibration in order to increase the accuracy of the UAS projects, and also to reduce field measurements.

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 Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 303
Author(s):  
Eloise S. Fogarty ◽  
David L. Swain ◽  
Greg M. Cronin ◽  
Luis E. Moraes ◽  
Derek W. Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Trials ◽  
Satellite System ◽  
False Positives ◽  
Sensor Data ◽  
Weather Data ◽  
Integrated Sensor ◽  
Detection Model ◽  
Independent Field ◽  
Grazing Sheep

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

scholarly journals Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tiannv Shi ◽  
Yongmei Guan ◽  
Lihua Chen ◽  
Shiyu Huang ◽  
Weifeng Zhu ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Production Process ◽  
Process Monitoring ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Powder Preparation ◽  
Spectroscopy Analysis ◽  
Online Process Monitoring ◽  
Online Process

Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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.

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