Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques
Ayuba Peter,
Luhutyit Peter Damuut,
Sa’adatu Abdulkadir
Issue:
Volume 6, Issue 1, June 2020
Pages:
1-9
Received:
28 January 2020
Accepted:
14 February 2020
Published:
26 February 2020
Abstract: In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.
Abstract: In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of p...
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Each Role of Short-term and Long-term Memory in Neural Networks
Issue:
Volume 6, Issue 1, June 2020
Pages:
10-15
Received:
20 February 2020
Accepted:
10 March 2020
Published:
24 March 2020
Abstract: Based on known functions of neuroscience the neural network that performs serial parallel conversion and its inverse transformation is presented. By hierarchy connecting the neural networks, the upper neural network that can process general time sequence data is constructed. The activity of the upper neural networks changes in response to the context structure inherent in the time series data and have both function of accepting and generating of general time series data. Eating behavior in animals in the early stages of evolution is also processing time series data, and it is possible to predict behavior although be limited short term by learning the contextual structure inherent in time series data. This function is the behavior of so-called short-term memory. Transition of the activation portion in this type of operation is illustrated. Although status of nervous system of the animal change according to the recognition by sensory organ and to the manipulation of the object by muscle in the vicinity of the animal itself, the evolved animals have in addition another nervous system so-called long-term memory or episodic memory being involved experience and prediction. The nervous system of long-term memory behaves freely but keeping consistency of the change in the environment. By the workings of long-term memory, lot of information are exchanged between fellows, and lot of time series data are conserved by characters in human society. In this paper, the model of the transfer of data between different nervous systems is shown using the concept of category theory.
Abstract: Based on known functions of neuroscience the neural network that performs serial parallel conversion and its inverse transformation is presented. By hierarchy connecting the neural networks, the upper neural network that can process general time sequence data is constructed. The activity of the upper neural networks changes in response to the conte...
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