A Location and Image based Plant Recognition and Recording System

• Main Authors: Tsung-Min Lin, 林琮閔
• Other Authors: Jen-Chang Liu
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/16612762316565024381

碩士 === 國立暨南國際大學 === 資訊工程學系 === 102 === Diversity of plants is rich in Taiwan. These plants are close to our life. For example, the fruits and the vegetables are used for food, the nectar is collected by butterflies. However, many plants are unfamiliar to us. People search the plant species by plant illustrated handbooks which use the plant’s family as the index. This query method is inefficient for people who are unfamiliar to plant’s family. Because of the difficulties to identify the plants for people, the thesis proposes to automatically identify the leaves applying content-based image retrieval assisted with Location-Based Search Engine. The first step is background subtraction. We adopt common methods which are the Otsu method, Color Slicing and GrabCut. We find the best method by evaluating image segmentation rate. Then the plant features are extracted using 1-D Fourier descriptors and 2-D Fourier descriptors. We conducted experiments to find effective features to design mobile application which can automatically recognize plant species. The application applies the idea of Crowdsourcing to attract people joining survey of plant information (example: location, description of plant), in order to collection a large number of plant images and geolocation. In this thesis, we collect plant image database based on common plant species in NCNU (National Chi Nan University) .In order to find the best background subtraction and plant features, we conducted a small-scale plant recognition experiment. For 21 plant species, 20 pictures for each plant species were collected at indoor. Through the experimental analysis, we find the best of background subtraction is GrabCut (custom mask) and the best plant features is 1-D Fourier descriptor. These methods are implemented on the mobile application. In the system, we collected 50 plant species and each plant species has 10 pictures taken at outdoor environment. Using Location-Based Search Engine to assist plant recognition, experimental result reached 78% Top 1 average precision.