Automatic Orchid Bottle Seedling Image Feature Extraction and Measurement based on Deep Mask Regions Convolutional Neural Networks

  • 楊 景倫

Student thesis: Doctoral Thesis

Abstract

This thesis aims to develop an automatic orchid bottle seedling image feature extraction and measurement algorithms based on mask regions convolutional neural networks (Mask R-CNN) for extracting the important growth features of orchid bottle seedlings to reach the goal of precise cultivation In this study to train and test the Mask R-CNN orchid bottle seedling images from different view angles were obtained from an orchid plantation factory in the southern Taiwan The original images collected from the factory were first labeled for their outlook contours such leaves and roots These contours are called as masks Then the labeled images were distorted to increase the diversity of the training and testing images Finally these images with their corresponding masks were served as the golden standards for the network training The Mask R-CNN-based image detection algorithm has been developed to extract the features of orchid bottle seedlings including leaf root green root tip white root tip yellow leaf green leaf effectively and automatically Ten different Mask R-CNN models were constructed for performance comparisons These ten models are the different layers of residual network (ResNet) including ResNet-26 ResNet-41 ResNet-50 ResNet-101 and ResNet-152 combined with fully convolutional network (FCN) and U-network (UNet) respectively The experimental results show that the ResNet-101-UNet outperforms the other models with higher average precision (AP) of feature extraction at 77 89% and its training time is 199 ms/image In addition to the feature extraction a feature measurement algorithm has been developed to measure/calculate the features such as the number of leaves and the length width and area of each leaf from orchid bottle seedling images detected by the Mask R-CNN models The experimental results show that the average percentage error of the area measurement of leaves is 16 47±6 41% due to the shading or blocking by other leaves or curly leaves while the average percentage error of the length measurement of roots is 7 28±3 01% The overall average errors of the feature measurements/calculations were satisfactory and thus validated the effectiveness of the proposed methods for the feature extraction of orchid bottle seedlings In the future we hope these algorithms can be applied to the orchid plantation industry and reach the goal of precise cultivation of orchid bottle seedlings
Date of Award2019
Original languageEnglish
SupervisorJeen-Shing Wang (Supervisor)

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