Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

• DOI: 10.11648/j.ajnna.20200601.11
• Publication Date: 2020-09-25T08:51:48Z
• Authors: Ayuba Peter; Luhutyit Peter Da...; Sa’adatu Abdulkadir
• Keywords: 2. Zero hunger; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 15. Life on land

In recent times, the phenomenal increase in population of people and livestock world has placed an enormous pressure on water land resources used by both crop farmers herders alike. Desertification, deforestation uncertainties climatic conditions Sub-Saharan Africa have led to massive movements search pasture with resultant conflicts local farm communities region. The inability find a lasting solution these problems persistent cases deteriorating relationships amongst which continued precipitate hostile consequences including loss lives, interruption annihilation family units some cases, whole are destroyed. This research attempts address problem inadequate grazing use advances Computational Intelligence Techniques determination optimum maturity maize, so as complement for Although challenge inherent determining maize is no means trivial, practice was hitherto based human perception, function experience over time. paper leverages Artificial Neural Networks (ANN) interfaced image processing Convolutional (pre-trained ResNet50 Network) ripeness grown Africa. Results obtained indicated 3.5% improvement classification accuracy pre-trained ANN model, providing stimulus further subject area. Therefore, this posits that could be sensitized possibility utilizing neural networks technique nearest future when made operational.