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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
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.
The Application Of A Visual Data Mining Framework To Determine Soil, Climate And Land-Use Relationships, Yunous Vagh
The Application Of A Visual Data Mining Framework To Determine Soil, Climate And Land-Use Relationships, Yunous Vagh
Research outputs 2012
In this research study, the methodology of action research dynamics and a case study was employed in constructing a visual data mining framework for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of a digital elevation model (DEM), soil and land use profiles that were juxtaposed with previously captured climatic data from fixed weather stations in Australia. In this pilot study, monthly rainfall profiles for a selected study area were used to identify areas of soil variability. The rainfall was sampled for the beginning (April) of the rainy season for …