Research on insect identification based on pattern recognition technology

Abstract

This paper presents a method of insect recognition using computer vision technology. First, we extracted fourteen features from images of some species of insects. These features are rectangularity, elongation, roundness, eccentricity, sphericity, lobation, compactness and seven Hu moment invariants. Second, a machine learning algorithm named Random Trees was employed, to play a role of a classifier in pattern recognition. The classifier was trained with the extracted features and the trained result was saved in a database. Then, the data can be loaded from the database into the algorithm, and then the algorithm can be used to recognize the species of insects which have been trained. In order to implement these algorithms, a series of software modules were developed to extract features, train algorithm, and do recognitions, based on an open source computer vision library named OpenCV, which is portable on Windows and Unix platforms.

Vision Based Entomology : A Survey

“…By using this method, the researchers claimed that they attained recognition about 90% as a whole [3]. Another similar research was done by the Yang et al [7] where the process of insect identification is focused on pattern recognition technology. Pattern recognition can be specifically defined as "the act of taking in raw data and taking an action based on the category of the pattern" [7].…”

“…Another similar research was done by the Yang et al [7] where the process of insect identification is focused on pattern recognition technology. Pattern recognition can be specifically defined as "the act of taking in raw data and taking an action based on the category of the pattern" [7]. They further explained that the process involve three stages which are, input data and information collected from the sample using sensor, feature extraction mechanism that compute numeric or symbolic data from the sample, and classification scheme that determine the group of the sample [7].…”

“…For success in insect recognition, they had collected several features of insect consists of rectangularity, elongation, roundness, eccentricity, sphericity, lobation, compactness and seven Hu moment invariants [7]. There are three classes of image features i.e colour, texture and edges.…”

“…There are three classes of image features i.e colour, texture and edges. However, edge extraction seems to be the best approach since it allowed minimal interference of light and noise [7]. This type of process employed the procedure of machine learning to train the algorithm to match with the image loaded and the result were saved in database.…”

“…Pattern recognition can be specifically defined as "the act of taking in raw data and taking an action based on the category of the pattern" [7]. They further explained that the process involve three stages which are, input data and information collected from the sample using sensor, feature extraction mechanism that compute numeric or symbolic data from the sample, and classification scheme that determine the group of the sample [7]. Meanwhile, image segmentation is defined as the process on which the image is segregate into several portions where each portions grouped together pieces that share similar characteristic such as intensity or texture in order to generate non-overlapping image [7].…”