Automatic moth detection from trap images for pest management

By Weiguang Ding, Graham W. Taylor. Computers and Electronics in Agriculture, 2016

340 citations recorded by the source corpus.

We propose a convolutional neural network-based automatic moth detection pipeline.We describe a set of methods for preprocessing raw moth trap images.Our method shows promising performance on a codling moth dataset from images collected in the field.Our species-agnostic method can be easily adapted to different pests and/or environments. Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.

Subfields: Cellular and Molecular Neuroscience, Genetics, Insect Science

View Paper: https://www.semanticscholar.org/paper/edd3553f1af38f2bb9150deef89b1aa082cda363

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A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “As per the discussion given in [76], LeNet is faster to deploy, acquiescent to parallel hardware, and efficient in performing small-scale image recognition problems.”