| Summary: | 碩士 === 國立暨南國際大學 === 資訊工程學系 === 107 === Taiwan has always been famous for its wide variety of species, especially the plants. Many kinds of plants grow everywhere. In the past, people, with strong eager and curiosity to know the plants they came across, had to look up the plant encyclopedia on the spot, or took pictures first and then went back home to search the information and identification of the plants. Out of the organs of the plant, flower is more eye-catching and pleasing. Nowadays, with the help of advanced technology, people can easily access smart devices, putting in keywords, even the images, to search on the internet. At present, most flower recognition systems follow the traditional recognition method. Users need to input images,after normalization, segmentation, feature extraction, recognition system then matches features from the dataset. The process is complicated and feature extraction needs manual analysis. The Artificial Intelligence (AI) technology develops promptly and promisingly.Amid AI, deep learning systems, repeatedly self-trained by learning large amounts of data, and automatically extract features instead of manual work.Therefore, it becomes more efficient, accurate and without manual interfering.
In this thesis, the author employs Deep Learning GPU Training System (DIGITS) and two network architectures, AlexNet and GoogLeNet, to undergo three sets of experiments. The images of fifty kinds of flowers in National Chi Nan University campus, ten images of each kind, totally five hundred images, serve as the database. Experiment (1) is the approach of using original image to train and test, and the recognition accuracy rates of AlexNet and GoogLeNet in Top1 are 64% and 64.8% respectively. Experiment (2) is the approach by removing the background of the image, the recognition accuracy rates are 57.2% and 63%. Experiment (3) is the approach of removing background, cutting out the blank area and focusing the flower to become normalized, the recognition accuracy rates are of 76% and 78.8%. As above mentioned, the Experiment (3) turns out significantly to be the best result. It is proved that the image will have a higher recognition rate after removing background and being normalized. In addition, the author also uses flower images in Pl@ntView, provided by ImageCLEF 2013 Plant identification, to do the experiment same as Experiment (3). There are 224 kinds of plants, including 3506 images in the dataset. Model training results show the recognition accuracy rates are 64.0625% and 70.5966%.
Finally, another purpose of this thesis is to developa convenient application for real time flower recognition, based on smart hand-held devices. The author makes an iOS APP for flower image recognition. Converting the trained Model by DIGITS into Core ML format and putting it into smart phone, a real time flower recognition APP has been implemented in iOS system. Through real time image in the phone, users can recognize flowers via Model and has result in real time. Users simply open the APP to capture real-time images. These images will be recognized in the model, and the Top1 results will be displayed.
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