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Malik Adeiza Rufai
Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach Charity Ojochogwu Egbunu; Matthew Tunde Ogedengbe; Terungwa Simon Yange; Malik Adeiza Rufai; Habibat Iye Muhammed
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.15

Abstract

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented random forest algorithm using Python programming language to predict the aforementioned climate parameters. The data used was gotten from the Nigerian Meteorological (NiMet) Agency, Lokoja, Kogi State between 1988 and 2018. The result shows that random forest algorithm is effective in climate prediction as the accuracy from the model based on the climatic factors considered was 94.64%. With this, farmers would be able to plan ahead to prevent the impact of the fluctuations in these climatic factors; thus, the yield of crops would be increased. This would dwarf the negative impact of food insecurity to the populace.
Violence Detection in Ranches Using Computer Vision and Convolution Neural Network Terungwa Simon Yange; Charity Ojochogwu Egbunu; Oluoha Onyekware; Malik Adeiza Rufai; Comfort Godwin
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.22

Abstract

This study engaged the convolutional neural network in curbing losses in terms of resources that farmers spends in treating animals where injuries must have emancipated from violence among other animals and in worst case scenario could eventually lead to death of animals. Animals in a ranch was the target and the study proposed a method that detects and reports activities of violence to ranchers such that farmers are relieved of the stress of close supervision and monitoring to avoid violence among animals. The scope of the study is limited to violence detection in cattle, goat, horse and sheep. Different machine learning models were built for each animal. The models yielded good results; the horse violence detection model had an outstanding performance of 93% accuracy, 93% accuracy for the sheep model, 88% accuracy for the goat model and 84% accuracy for the cattle model.
Co-Authors Charity Ojochogwu Egbunu Comfort Godwin Habibat Iye Muhammed Matthew Tunde Ogedengbe Oluoha Onyekware Terungwa Simon Yange