Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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A Meta-Heuristics based decision support system for optimal crop planning
(MCSER Publishing, 2014-09) Adekanmbi, Oluwole; Green, Paul
This paper presents a meta-heuristic based decision support system to support farmers with optimal strategic decision making in the crop planning system. The analysis and modelling of crop planning decision making process are attractive for producing formalized knowledge on cropping plans in an agricultural farm. The formalization of the decision making process is generally becoming a crucial focal point for developing decision support systems that go beyond the limitation of formerly developed prescriptive approaches. The methodology of this research strikes a balance between mathematical formulations of crop planning problems and effective implementation of crop planning decision models. The new approach used to solve this problem is applicable to both medium and large scale farming; it is also integrated a systemic framework to build better decision support systems in agriculture.
Multiobjective optimization of crop-mix planning using generalized differential evolution algorithm
(2015) Adekanmbi, Oluwole; Olugbara, Oludayo O.
This paper presents a model for constrained multiobjective optimization of mixed-cropping planning. The decision challenges that are normally faced by farmers include what to plant, when to plant, where to plant and how much to plant in order to yield maximum output. Consequently, the central objective of this work is to concurrently maximize net profit, maximize crop production and minimize planting area. For this purpose, the generalized differential evolution 3 algorithm was explored to implement the mixed-cropping planning model, which was tested with data from the South African grain information service and the South African abstract of agricultural statistics. Simulation experiments were conducted using the non-dominated sorting genetic algorithm II to validate the performance of the generalized differential evolution 3 algorithm. The empirical findings of this study indicated that generalized differential evolution 3 algorithm is a feasible optimization tool for solving optimal mixed-cropping planning problems.
Predicting the microbial safety of irrigation water and fresh produce : a collaborative approach
(2013-06-11) Ijabadeniyi, Oluwatosin Ademola; Olugbara, Oludayo O.
Outbreak of food borne illnesses as a result of consumption of fresh vegetables and fruits is occurring regularly. In USA for example, it has become business as usual to hear and read about fresh produce recalls. Although, the increase has been attributed to many factors it is however more important to find solution to the problem. An effective solution will be a proactive approach such as prediction and forecasting which are not new in the field of meteorology. For many years now, meteorologists have been predicting the weather. It is indeed high time that food scientists in collaboration with other professionals found out dependable and realistic methods to predict the presence of pathogens in irrigation water and fresh produce. In this review, several prediction tools such as factor analysis, artificial neural network, support vector machine, logistic regression analysis, partial least square and ‘nanosensing’ were discussed. The problem of produce safety may in fact be solved when food scientist collaborate with I.T professionals, biotechnologists and others.