Automatic ladybird beetle detection using deep-learning models.

Automatic ladybird beetle detection using deep-learning models.

Blog Article

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics.Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system.First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and deus gorras active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image.Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output.

This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project.The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks.These successful swisse high strength magnesium powder berry results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.