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Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
1h-Cyclopropabenzene And 1h-Cyclopropa[B]Naphthalene Fumigation Suppresses Climacteric Ethylene And Respiration Rates And Modulates Fruit Quality In Long-Term Controlled Atmosphere-Stored ‘Gold Rush’ Pear Fruit, Vijay Yadav Tokala, Zora Singh, Poe Nandar Kyaw
1h-Cyclopropabenzene And 1h-Cyclopropa[B]Naphthalene Fumigation Suppresses Climacteric Ethylene And Respiration Rates And Modulates Fruit Quality In Long-Term Controlled Atmosphere-Stored ‘Gold Rush’ Pear Fruit, Vijay Yadav Tokala, Zora Singh, Poe Nandar Kyaw
Research outputs 2014 to 2021
‘Gold Rush’ pear (Pyrus communis L.) is a russet-coloured fruit with soft buttery textured flesh and is gaining wide popularity in Australia and other countries along with other pear cultivars. The fruit are sensitive to ethylene, and exposure even at very low concentrations significantly reduces the storage duration as well as fruit quality during storage. The efficacy of two new ethylene antagonist compounds, namely, 1H-cyclopropabenzene (BC) and 1H-cyclopropa[b]naphthalene (NC), as well as 1-methylcyclopropene (1-MCP) in regulating ethylene production, respiration rates and maintaining the fruit quality of ‘Gold Rush' pear during 150 d and 200 d of controlled atmosphere (CA) storage …
Led Illumination Spectrum Manipulation For Increasing The Yield Of Sweet Basil (Ocimum Basilicum L.), Md Momtazur Rahman, Mikhail Vasiliev, Kamal Alameh
Led Illumination Spectrum Manipulation For Increasing The Yield Of Sweet Basil (Ocimum Basilicum L.), Md Momtazur Rahman, Mikhail Vasiliev, Kamal Alameh
Research outputs 2014 to 2021
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Manipulation of the LED illumination spectrum can enhance plant growth rate and development in grow tents. We report on the identification of the illumination spectrum required to significantly enhance the growth rate of sweet basil (Ocimum basilicum L.) plants in grow tent environments by controlling the LED wavebands illuminating the plants. Since the optimal illumination spectrum depends on the plant type, this work focuses on identifying the illumination spectrum that achieves significant basil biomass improvement compared to improvements reported in prior studies. To be able to optimize the illumination spectrum, several …
A Novel Method For Detecting Morphologically Similar Crops And Weeds Based On The Combination Of Contour Masks And Filtered Local Binary Pattern Operators, Vi Nguyen Thanh Le, Selam Ahderom, Beniamin Apopei, Kamal Alameh
A Novel Method For Detecting Morphologically Similar Crops And Weeds Based On The Combination Of Contour Masks And Filtered Local Binary Pattern Operators, Vi Nguyen Thanh Le, Selam Ahderom, Beniamin Apopei, Kamal Alameh
Research outputs 2014 to 2021
Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of …
Species Distribution Model Of Invasive Alien Species Acacia Nilotica For Central-Eastern Indonesia Using Biodiversity Climate Change Virtual Laboratory (Bccvl), Sutomo, Eddie Van Etten
Species Distribution Model Of Invasive Alien Species Acacia Nilotica For Central-Eastern Indonesia Using Biodiversity Climate Change Virtual Laboratory (Bccvl), Sutomo, Eddie Van Etten
Research outputs 2014 to 2021
Climate change may facilitate alien species invasion into new areas. This study uses Biodiversity and Climate Change Virtual Laboratory to develop a species distribution model (SDM) of Acacia nilotica (L.) Willd. ex Delile. based upon its naturalized distribution to project the potential distribution of A. nilotica throughout tropical environment of Indonesia under current and future climate conditions. Global biodiversity information facility database was utilized to obtain the species occurrences data. The climate factors were precipitation and temperature layers, available in Worldclim current conditions (1950-2000) at 2.5 arcmin. We used Generalized Linear Model. The result was then projected to the year …
Mobile Applications For Indian Agriculture Sector: A Case Study, Pratik Shah, Niketa Gandhi, Leisa Armstrong
Mobile Applications For Indian Agriculture Sector: A Case Study, Pratik Shah, Niketa Gandhi, Leisa Armstrong
Research outputs 2014 to 2021
Government, private agencies and the general public are often interested in the decisions made by the Indian farmers as they have large influences beyond the farm boundary. Over many years, the process of adoption of new technologies and policies in the Indian agricultural sector has received considerable academic attention highlighting the role of many social, financial and other influences on their decision making. The Indian government and other development agencies promote income generating projects as a way of encouraging growth through increased agricultural production and the protection of the natural resource base. The impact of new technology to economic growth …
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 …
A Network That Really Works - The Application Of Artificial Neural Networks To Improve Yield Predictions And Nitrogen Management In Western Australia, Jinsong Leng, Andreas Neuhaus, Leisa Armstrong
A Network That Really Works - The Application Of Artificial Neural Networks To Improve Yield Predictions And Nitrogen Management In Western Australia, Jinsong Leng, Andreas Neuhaus, Leisa Armstrong
Research outputs 2014 to 2021
Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results …
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
Research outputs 2014 to 2021
This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, …
Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson
Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson
Research outputs 2014 to 2021
This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis …
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 …
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.
A Survey Of Image Processing Techniques For Agriculture, Lalit Saxena, Leisa Armstrong
A Survey Of Image Processing Techniques For Agriculture, Lalit Saxena, Leisa Armstrong
Research outputs 2014 to 2021
Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts.
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 …