ASSESSING INSECT GROWTH USING IMAGE ANALYSIS
Image analysis represents an invaluable tool in processing and analyzing biological data in an expeditious and reliable way. This paper describes the design and implementation of an image analysis system for the automatic assessment of the growth of Heliothis-zea insects from color images. Specifically, the Heliothis zea is a corn earworm eating corn crops. Biotech researchers are interested in developing insecticidal bio-toxins with the best performance to kill or stunt the growth of the pest. Currently, assessing the effectiveness of different bio-toxin solutions is done mostly manually by biotech experts. The goal of this study is to investigate the use of image analysis for automating and improving the efficiency of this process. In this context, we have developed a prototype system for assessing insect growth from color images which contains three main stages: (1) insect segmentation from background, (2) region processing and feature extraction, and (3) categorization of insect growth. A probabilistic model based on mixtures of Gaussians has been adopted to segment the insect images. Also, back-propagation neural networks have been trained to classify the instar stage and life stage. The proposed system has been evaluated on a set of real images.
mixtures of gaussians neural networks image segmentation pattern classification