First steps toward an electronic field guide for plants

Abstract

We describe an ongoing project to digitize information about plant specimens and make it available to botanists in the field. This first requires digital images and models, and then effective retrieval and mobile computing mechanisms for accessing this information. We have almost completed a digital archive of the collection of type specimens at the Smithsonian Institution Department of Botany. Using these and additional images, we have also constructed prototype electronic field guides for the flora of Plummers Island. Our guides use a novel computer vision algorithm to compute leaf similarity. This algorithm is integrated into image browsers that assist a user in navigating a large collection of images to identify the species of a new specimen. For example, our systems allow a user to photograph a leaf and use this image to retrieve a set of leaves with similar shapes. We measured the effectiveness of one of these systems with recognition experiments on a large dataset of images, and with user studies of the complete retrieval system. In addition, we describe future directions for acquiring models of more complex, 3D specimens, and for using new methods in wearable computing to interact with data in the 3D environment in which it is acquired.

Shape Classification Using the Inner-Distance

“…For example, during a filed test, a botanist can input a picture of an unknown leaf to the system and get the most visually similar leaves in a database. A detailed description of the system can be found in [2]. The task is very challenging because it requires querying from a database containing more than 100 species and real-time performance requires an efficient algorithm.…”

“…We design a dynamic programming method for silhouette matching that is fast and accurate since it utilizes the ordering information between contour points. Both approaches are tested on a variety of shape databases, including an articulated shape database, 1 MPEG7 CE-Shape-1 shapes, Kimia's silhouette [40], [39], ETH-80 [26], a Swedish leaf database [42], and a Smithsonian leaf database [2]. The excellent performance demonstrates the inner-distance's ability to capture part structures (not just articulations).…”

Automatic classification of legumes using leaf vein image features

“…15 Several approaches have been proposed, including leaf shape [12, 1,4,7,20,5], 1 color information [11,15] and leaf texture analysis [8,2]. 2 Although all these approaches are valid, they are not useful when dealing 3 with species having similar leaf size, color, shape and texture features.…”

Variant-Order Statistics based Model for RealTime Plant Species Recognition

“…For a leaf class i consists of a set of N leaf images CN =l 1 , l 2 ,…, l N . Each class i is characterized by the collection of its descriptors values obtained during a training phase.…”

“…However, different plant species share a very close relationship to human beings. Therefore, interest for visual classification methods of plant species have grown recently [1,2,3].…”