Rahman, Muhammad Ariff Azizul and Gunawan, Teddy Surya and Kartiwi, Mira (2019) Herb leaves pattern recognition using digital microscope and deep learning. In: International Conference on Universal Wellbeing (ICUW2019), 4 - 6 Dec 2019, Kuala Lumpur, Malaysia.
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Abstract
Herb leaves are not fully exploited for their benefits due to the problem arise from difficulties in identifying the type of plants. Visual-based pattern recognition via microscopic imaging provides high precision and accuracy for identification that easily outperforms the limitations of human perception. A three-class dataset of 255 leaf vein images - betel, noni and ortosiphon stamineus, were captured by a digital microscope was constructed as a training set for a visual-based pattern recognition system. A simple visual-based pattern recognition system was constructed by using MATLAB and deep learning. Transfer learning was applied on a pretrained VGG-16 network with a validation accuracy of 91.03%. Further fine-tuning was applied on the network resulting an increase of accuracy to 98.72%.
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