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Physical Sciences and Mathematics Commons™
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Soil Seed Banks Of Fringing Salt Lake Vegetation In Arid Western Australia - Density, Composition And Implications For Postmine Restoration Using Topsoil, Eddie J. Van Etten, Brett Neasham, Sarah Dalgleish
Soil Seed Banks Of Fringing Salt Lake Vegetation In Arid Western Australia - Density, Composition And Implications For Postmine Restoration Using Topsoil, Eddie J. Van Etten, Brett Neasham, Sarah Dalgleish
Research outputs 2014 to 2021
Although studies of seed banks in arid ecosystems are commonplace, they are lacking for the large arid zone of Western Australia. Across the six major plant communities fringing a large salt lake within this zone, topsoil (0-5 cm depth) was collected from 12 to 36 sites per community. Samples were dried, spread out on a bed of vermiculite in seedling trays and placed in a well-watered glasshouse to determine the readily germinable component of the soil seed bank. Subsamples of topsoil were treated with smoke water, hot water or flooding to help determine seed bank of species with dormancy mechanisms. …
Application Of Cellular Neural Networks And Naive Bayes Classifier In Agriculture, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Application Of Cellular Neural Networks And Naive Bayes Classifier In Agriculture, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
This article describes the use of Cellular Neural Networks (a class of Ordinary Differential Equation (ODE)), Fourier Descriptors (FD) and NaiveBayes Classifier (NBC) for automatic identification of images of plant leaves. The novelty of this article is seen in the use of CNN for image segmentation and a combination FDs with NBC. The main advantage of the segmentation method is the computation speed compared with other edge operators such as canny, sobel, Laplacian of Gaussian (LoG). The results herein show the potential of the methods in this paper for examining different agricultural images and distinguishing between different crops and weeds …
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Research outputs 2014 to 2021
Spatial data mining helps to identify interesting patterns from the spatial data sets. However, geo spatial data requires substantial data pre-processing before data can be interrogated further using data mining techniques. Multi-dimensional spatial data has been used to explain the spatial analysis and SOLAP for pre-processing data. This paper examines some of the methods for pre-processing of the data using Arc GIS 10.2 and Spatial Analyst with a case study dataset of a watershed.
Decision Support System Data For Farmer Decision Making, Pornchai Taechatanasat, Leisa Armstrong
Decision Support System Data For Farmer Decision Making, Pornchai Taechatanasat, Leisa Armstrong
Research outputs 2014 to 2021
The capacity of farmers and agricultural scientists to be able to make in-season decisions is dependent on accurate climate, soil and plant data. This paper will provide a review of the types of environmental and crop data that can be collected by sensors which can used for decision support systems (DSS) or be further interrogated for real time data mining and analysis. This paper also presents a review of the data requirements for agricultural decision making by firstly reviewing decision support frameworks and agricultural DSSs, data acquisition, sensors for data acquisition and examples of data incorporation for agricultural DSSs.
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature …