Herb leaves pattern recognition using digital microscope and deep learning - Rahman, Muhammad Ariff Azizul; Gunawan, Teddy Surya; Kartiwi, Mira (Persatuan Saintis Muslim Malaysia (PERINTIS), 2019)
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%.