Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
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 …
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Research outputs 2014 to 2021
Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used …