title: Assessment of Standing Herbage Dry Matter Using A Range Imaging Sytem

authors: Benseman, Mark

subjects: Herbage

abstract: It has been known for a long time that a device that could quickly and accurately ascertain dry matter content would be very useful to pastoral farmers. Despite many years of various products being developed there is still a lack of consistent and accurate measurements available. We present a proof of concept using a time of flight  imaging system to measure standing herbage dry matter. Scenes of herbage were captured using the SoftKinitec DS325 range imaging camera. Each scene included range and intensity images as well as colour images. Simple statistical analysis of the images was carried out and related to dry matter content. Twenty data points were gathered in late autumn growing conditions. The best correlation achieved was 0.9 with a standard deviation of 337 kgDM/ha. This was achieved used a multivariate linear regression. The predictors used were average depth, and standard deviations of both depth and intensity frames. The worst correlation achieved using a multivariate linear regression was 0.89 with a standard deviation of 365 kgDM/ha.  Thirteen data points were also gathered during severe drought conditions. The same statistical analysis resulted in a best fit of 0.52 and a standard deviation of 533 kgDM/ha. Range cameras show promise when compared to currently available methods of dry matter measurement.

text: Assessment of Standing Herbage Dry Matter Using A Range Imaging Sytem It has been known for a long time that a device that could quickly and accurately ascertain dry matter content would be very useful to pastoral farmers. Despite many years of various products being developed there is still a lack of consistent and accurate measurements available. We present a proof of concept using a time of flight  imaging system to measure standing herbage dry matter. Scenes of herbage were captured using the SoftKinitec DS325 range imaging camera. Each scene included range and intensity images as well as colour images. Simple statistical analysis of the images was carried out and related to dry matter content. Twenty data points were gathered in late autumn growing conditions. The best correlation achieved was 0.9 with a standard deviation of 337 kgDM/ha. This was achieved used a multivariate linear regression. The predictors used were average depth, and standard deviations of both depth and intensity frames. The worst correlation achieved using a multivariate linear regression was 0.89 with a standard deviation of 365 kgDM/ha.  Thirteen data points were also gathered during severe drought conditions. The same statistical analysis resulted in a best fit of 0.52 and a standard deviation of 533 kgDM/ha. Range cameras show promise when compared to currently available methods of dry matter measurement.

year: 2013

title: The online performance estimation framework: heterogeneous ensemble learning for data streams

authors: van Rijn, Jan N.

subjects: Science & Technology

abstract: Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, comprised of fundamentally different model types. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. We study the use of heterogeneous ensembles for data streams. We introduce the Online Performance Estimation framework, which dynamically weights the votes of individual classifiers in an ensemble. Using an internal evaluation on recent training data, it measures how well ensemble members performed on this and dynamically updates their weights. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging, while being significantly faster. All experimental results from this work are easily reproducible and publicly available online.

text: The online performance estimation framework: heterogeneous ensemble learning for data streams Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, comprised of fundamentally different model types. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. We study the use of heterogeneous ensembles for data streams. We introduce the Online Performance Estimation framework, which dynamically weights the votes of individual classifiers in an ensemble. Using an internal evaluation on recent training data, it measures how well ensemble members performed on this and dynamically updates their weights. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging, while being significantly faster. All experimental results from this work are easily reproducible and publicly available online.

year: 2018

title: Translated application interfaces - issues of engagement

authors: Mato, Paora James

abstract: In New Zealand, English is the language that dominates contemporary technologies. Usability testing was completed on a range of applications, available with a Māori-language interface, to gauge levels of awareness, engagement and perception. Nearly all of the respondents were unaware of the availability of these interfaces but most indicated they would prefer to use the Māori-language versions. In terms of engagement and usability, users initially engaged using Māori but switched to English when they wanted to quickly complete the task at hand. Few remained fully engaged with the Māori-language interfaces. High levels of language switching were reported and some frustration as the participants encountered new and unfamiliar uses of words. At face value the feedback suggests the translated interfaces contained unnecessary complications and that better design and content might have enhanced the user experience. However, there is evidence that extended use would enable users to become more familiar with the interfaces alluding to initial barriers created by a previous competence in another language -- in this case English. With this previous competence in mind it might be more useful to employ design concepts that would alleviate initial difficulties and serve to keep the user engaged in the target language for longer periods of time.

text: Translated application interfaces - issues of engagement In New Zealand, English is the language that dominates contemporary technologies. Usability testing was completed on a range of applications, available with a Māori-language interface, to gauge levels of awareness, engagement and perception. Nearly all of the respondents were unaware of the availability of these interfaces but most indicated they would prefer to use the Māori-language versions. In terms of engagement and usability, users initially engaged using Māori but switched to English when they wanted to quickly complete the task at hand. Few remained fully engaged with the Māori-language interfaces. High levels of language switching were reported and some frustration as the participants encountered new and unfamiliar uses of words. At face value the feedback suggests the translated interfaces contained unnecessary complications and that better design and content might have enhanced the user experience. However, there is evidence that extended use would enable users to become more familiar with the interfaces alluding to initial barriers created by a previous competence in another language -- in this case English. With this previous competence in mind it might be more useful to employ design concepts that would alleviate initial difficulties and serve to keep the user engaged in the target language for longer periods of time.

year: 2015