Implementation of an electronic nose for continuous odour monitoring in a poultry shed

Continuous odour monitoring technologies are necessary to understand the complex odour-generating mechanisms within poultry housing as well as to identify strategies to reduce the impact of odour emissions on local communities. To evaluate electronic nose (EN) technologies for continuously assessing odour concentration in poultry housing, a mobile laboratory containing an electronic nose and an associated sample delivery system was deployed to a commercial poultry farm and tested over a broiler production cycle. The results demonstrated that it was possible to develop a model to allow an electronic nose to provide a semi-continuous measurement of odour concentrations. The electronic nose was also able to demonstrate the influence of shed conditions on odour emissions.

A digital imaging technique to quantify the incidence of internal browning in potato (Solanum tuberosum) tubers

Internal browning disorders, including brown fleck (BF), in potato (Solanum tuberosum) tubers greatly reduce tuber quality, but the causes are not well understood. This is due, in part, to the highly variable data provided by visual value-based rating systems. A digital imaging technique was developed to quantify accurately the incidence of internal browning in potato tubers. Images of tuber sections were scanned using a flatbed scanner and digitally enhanced to highlight tuber BF lesions, and the area of affected tissue calculated using pixel quantification software. Digital imaging allowed for the determination of previously unused indices of the incidence and severity of internal browning in potato tubers. Statistical analysis of the comparison between digitally derived and visual-rating BF data from a glasshouse experiment showed that digital data greatly improved the delineation of treatment effects. The F-test probability was further improved through square root or logarithmic data transformations of the digital data, but not of the visual-rating data. Data from a field experiment showed that the area of tuber affected by BF and the number of small BF lesions increased with time and with increase in tuber size. The results from this study indicate that digital imaging of internal browning disorders of potato tubers holds much promise in determining their causes that heretofore have proved elusive.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Multispectral imaging and genomic mapping

Identifying QTLs linked to malting and protein levels in barley using multispectral image data.

The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics

The global importance of grasslands is indicated by their extent; they comprise some 26% of total land area and 80% of agriculturally productive land. The majority of grasslands are located in tropical developing countries where they are particularly important to the livelihoods of some one billion poor peoples. Grasslands clearly provide the feed base for grazing livestock and thus numerous high-quality foods, but such livestock also provide products such as fertilizer, transport, traction, fibre and leather. In addition, grasslands provide important services and roles including as water catchments, biodiversity reserves, for cultural and recreational needs, and potentially a carbon sink to alleviate greenhouse gas emissions. Inevitably, such functions may conflict with management for production of livestock products. Much of the increasing global demand for meat and milk, particularly from developing countries, will have to be supplied from grassland ecosystems, and this will provide difficult challenges. Increased production of meat and milk generally requires increased intake of metabolizable energy, and thus increased voluntary intake and/or digestibility of diets selected by grazing animals. These will require more widespread and effective application of improved management. Strategies to improve productivity include fertilizer application, grazing management, greater use of crop by-products, legumes and supplements and manipulation of stocking rate and herbage allowance. However, it is often difficult to predict the efficiency and cost-effectiveness of such strategies, particularly in tropical developing country production systems. Evaluation and on-going adjustment of grazing systems require appropriate and reliable assessment criteria, but these are often lacking. A number of emerging technologies may contribute to timely low-cost acquisition of quantitative information to better understand the soil-pasture-animal interactions and animal management in grassland systems. Development of remote imaging of vegetation, global positioning technology, improved diet markers, near IR spectroscopy and modelling provide improved tools for knowledge-based decisions on the productivity constraints of grazing animals. Individual electronic identification of animals offers opportunities for precision management on an individual animal basis for improved productivity. Improved outcomes in the form of livestock products, services and/or other outcomes from grasslands should be possible, but clearly a diversity of solutions are needed for the vast range of environments and social circumstances of global grasslands.