Artificial olfaction system for on-site odour measurement

Odour impacts and concerns are an impediment to the growth of the Australian chicken meat industry. To manage these, the industry has to be able to demonstrate the efficacy of its odour reduction strategies scientifically and defensibly; however, it currently lacks reliable, cost effective and objective tools to do so. This report describes the development of an artificial olfaction system (AOS) to measure meat chicken farm odour. This report describes the market research undertaken to determine the demand for such a tool, the development and evaluation of three AOS prototypes, data analysis and odour prediction modelling, and the development of two complementary odour measurement tools, namely, a volatile organic compound (VOC) pre-concentrator and a field olfactometer. This report is aimed at investors in poultry odour research and those charged with, or interested in, assessment of odour on chicken farms, including farm managers, integrators, their consultants, regulators and researchers. The findings will influence the focus of future environmental odour measurement research

Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon

Visible near infrared reflectance spectroscopy (vis–NIR) is an increasingly popular measurement method that can provide cheaper and faster predictions of soil properties, including soil organic carbon content (SOC). The spectroscopic prediction method relies significantly on the development of regressions of data in spectral databases or libraries. While the vis–NIR estimation of SOC was developed in controlled laboratory conditions, its natural development in recent years has been to perform the vis–NIR measurements in situ, where soil spectra are recorded under field conditions. However, environmental factors, such as soil moisture content, have been shown to affect soil spectra, making the use of regressions derived using soil spectral libraries difficult. Direct standardization (DS) and external parameter orthogonalisation (EPO) are two methods that were proposed for the correction of variable moisture conditions and other environmental factors. In this study, we compared DS and EPO on a set of 150 soil samples (3 depths from each of 50 soil cores) from a farm in New Zealand. The samples were re-wetted under controlled conditions, and spectra were recorded at nine different moisture levels. Our results show that DS and EPO are two effective strategies to mitigate the effects of soil water content on vis–NIR spectra. While DS and EPO results were similar when a large number of soil cores were reserved for calibrating the moisture correction methods, SOC predictions using the EPO correction significantly outperformed those using the DS correction for a lower number of cores (5 cores, 15 samples)

rs-local data-mines information from spectral libraries to improve local calibrations

Diffuse reflectance spectroscopy in the visible–near infrared (vis–NIR) and mid infrared (mid-IR) can be used to estimate soil properties, such as organic carbon (C) content. Compared with conventional laboratory methods, it enables practical and inexpensive measurements at finer spatial and temporal resolutions, which are needed to improve the assessment and management of soil and the environment. Measurements of soil properties with spectra require empirical calibration and soil spectral libraries (SSL) have been developed for this purpose at the regional, continental and global scales. Calibrations derived with these SSLs, however, are often shown to predict poorly at local sites. Here we present a new method, rs-local, that uses a small representative set of site-specific (or ‘local’) data and re-sampling techniques to select a subset of data from a large vis-NIR SSL to improve calibrations at the site. We demonstrate the implementation of rs-local by estimating soil organic C in two fields with different soil types, one in Australia and one in New Zealand. We found that with as few as 12 to 20 site-specific samples and the SSL, training datasets derived with rs-local could accurately predict soil organic C concentrations. Predictions with the rs-local data were comparable to, or better than those made with site-specific calibrations with up to 300 samples. Our method outperformed other published ‘local’ spectroscopic techniques that we tested. Thus, rs-local can effectively improve both the accuracy and financial viability of soil spectroscopy

Application of millimetre wave radar sensor to environment mapping in surface mining

This paper presents the application of a millimetre wave radar technology to environment mapping in surface mining. Sensor requirements for ranging and surface profiling in shovel and dragline operations were determined based on machines performance requirements. Frequency Modulated Continuous Wave (FMCW) technique was selected to achieve the correct range resolution while Fast Fourier Techniques (FFT) was used to extract the range data from the radar output. Radar data processing was undertaken using Stochastic Environment Representation technique in real-time. The results of field trials show successful radar performance in terms of the system's accurate measurement of excavation terrain, real-time imaging, robustness, reliability and penetration through dust and water

Proximal soil sensing. An effective approach for soil measurements in space and time

This chapter reviews proximal soil sensing (PSS). Our intent is for it to be a source of up-to-date information on PSS, the technologies that are currently available and their use for measuring soil properties. We first define PSS and discuss the sampling dilemma. Using the range of frequencies in the electromagnetic spectrum as a framework, we describe technologies that can be used for PSS, including electrochemical and mechanical sensors, telemetry, geographic positioning and elevation, multisensor platforms, and core measuring and down-borehole sensors. Because soil properties can be measured with different proximal soil sensors we provide examples of the alternative techniques that are available for measuring soil properties. We also indicate the developmental stage of technologies for PSS and the current approximate cost of commercial sensors. Our discussion focuses on the development of PSS over the past 30. years and on its current state. Finally, we provide a short list of general considerations for future work and suggest that we need research and development to: (i) improve soil sampling designs for PSS, (ii) define the most suitable technique or combination of techniques for measuring key soil properties, (iii) better understand the interactions between soil and sensor signals, (iv) derive theoretical sensor calibrations, (v) understand the basis for local versus global sensor calibrations, (vi) improve signal processing, analysis and reconstruction techniques, (vii) derive and improve methods for sensor data fusion, and (viii) explore the many and varied soil, agricultural, and environmental applications where proximal soil sensors could be used. PSS provides soil scientists with an effective approach to learn more about soils. Proximal soil sensors allow rapid and inexpensive collection of precise, quantitative, fine-resolution data, which can be used to better understand soil spatial and temporal variability. We hope that this review raises awareness about PSS to further its research and development and to encourage the use of proximal soil sensors in different applications. PSS can help provide sustainable solutions to the global issues that we face: food, water, and energy security and climate change

Millimetre wave radar visualisation system: Practical approach to transforming mining operations

This paper describes the development of millimetre-wave frequency modulated continuous wave radar and its application in the mining industry. Assessment of various sensors: laser, sonar, visual and microwave radar, against the mm wave radar sensor proves radar performance superiority in difficult mining conditions. The implementation and performance verification of the radar sensor for range and 3D profiling were undertaken in underground environment for stope fill and orepass monitoring and in surface mines for dragline environmental mapping and rope shovel bucket and dig face imaging. The millimetre wave radar visualisation system development, testing, on-site applications and the benefits to the mining industry are hereby discussed

Assessing the Sensitivity of Site-Specific Lime and Gypsum Recommendations to Soil Sampling Techniques and Spatial Density of Data Collection in Australian Agriculture: A Pedometric Approach

There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has not been well described. The work presented here investigates the spatial errors of gypsum and lime recommendations based on industry-standard blanket-rate and zone-based variable rate application, as well as the more advanced pedometric approaches – ordinary kriging (OK) and regression kriging (RK). All methods were tested at sampling densities between 0.1–3 samples/ha for a 108 ha broadacre site in central NSW, Australia. Whilst previous work has tested the effect of sampling density on the spatial predictive performance of OK and RK, here we assess prediction accuracy as the error associated with soil management decisions based on their results (i.e., the over- and under-application error of gypsum and lime applications) in conjunction with the RMSE of prediction for soil pH and exchangeable sodium percentage (ESP). The uncertainty of each method is also tested to observe the effect of random initialisation on predictive performance. Results indicated that RK provided superior spatial predictions across all sampling densities for the application of gypsum and lime, with a blanket-rate application providing the worse results, with over- and under-application errors exceeding 200 t and 300 t respectively for 40–60 cm treatment for the entire field. Interestingly, the spatial accuracy of amendment application increased to a sampling density of 0.5 samples/ha for RK, with minimal improvement thereafter, suggesting that meaningful soil amelioration advice can be attained proximal to this density

Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

Soil information is needed for environmental monitoring to address current concerns over food, water and energy securities, land degradation, and climate change. We developed the Soil Condition ANalysis System (SCANS) to help address these needs. It integrates an automated soil core sensing system (CSS) with statistical analytics and modeling to characterize soil at fine depth resolutions and across landscapes. The CSS's sensors include a ?-ray attenuation densitometer to measure bulk density, digital cameras to image the measured soil, and a visible-near-infrared (vis-NIR) spectrometer to measure iron oxides and clay mineralogy. The spectra are also modeled to estimate total soil organic carbon (C), particulate, humus, and resistant organic C (POC, HOC, and ROC, respectively), clay content, cation exchange capacity (CEC), pH, volumetric water content, available water capacity (AWC), and their uncertainties. Measurements of bulk density and organic C are combined to estimate C stocks. Kalman smoothing is used to derive complete soil property profiles with propagated uncertainties. The SCANS provides rapid, precise, quantitative, and spatially explicit information about the properties of soil profiles with a level of detail that is difficult to obtain with other approaches. The information gained effectively deepens our understanding of soil and calls attention to the central role soil plays in our environment