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

Domain:

agriculture

Record type:

paper
Creator:
OyeOsa
Publisher:
Zenodo
Host:avatar
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.

Visit

doi.orgzenodo.org

Tasks

computer visionimage classification

Tags

Deep neural networkImage processingConvolutionComputer visionQuality checksAgriculture

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcodeOpen Accessinfo:eu-repo/semantics/openAccess

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