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Simplified Image Classification for Nigeria's Agricultural Produce through Deep Neural Network Techniques

Authors: Oyebode KO, Osah UJ

DOI Info:


Image processing techniques can support quality checks for agricultural produce. Noting the relevance to the Nigerian context, this paper details the development of a computer vision system for automatic screening of produce. The development included four phases, namely: model development, training, graphical user interface (GUI) creation, and module testing. Model development involved a convolution process with capacity to extract useful features from the image of an agricultural produce. Training enabled the created model to learn crucial image structures based on fine-tuned mask parameters that support the classification of similar images efficiently. The GUI enables non-technical users to train new models, as well as carry out single and multiple classifications. The testing phase enables the evaluation of the system. Images are tested using a pre-trained model as well as without a pre-trained model. The model backed by the pre-trained model performed better than that not associated with a pre-trained model. Importantly, whereas the application of deep neural networks has been explored, a useful contribution here is the introduction of a user friendly GUI to underpin its employment.

Affiliations: Department of Design and New Media, School of Media and Communication, Pan-Atlantic University, PMB 73688, Lagos, Lagos State, Nigeria.
Keywords: Deep Neural Network, Image Processing, Convolution, Computer Vision, Quality Checks, Agriculture
Published date: 2022/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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Chemical Engineering Department, Faculty of Engineering, University of Benin, PMB 1154, Ugbowo, Benin City, Edo State, Nigeria.