Insects identification with convolutional neural network technique in the sweet corn field

Abstract

A method to identify the type of insects with accurate and precise results is of importance. Nowadays, an automatic object identification system with increased accuracy, improved speed, and less cost have been developed. Convolutional Neural Network (CNN) implementation for image identification or classification can be done by collecting large-scale datasets containing hundreds to millions of images to study the many parameters involved in the network. This research was conducted to develop and apply the CNN model to identify eight species of insects in the sweet corn field in Thailand. Those insects were Calomycterus sp., Rhopalosiphum maidis, Frankliniella williamsi, Spodoptera frugiperda, Spodoptera litura, Ostrinia furnacalis, Mythimna separata, and Helicoverpa armigera. The CNN model in this research was built with four convolutional layers, which consist of Conv2D, batch normalization, max pooling, dropout sublayer, and a fully-connected layer. in total, 5568 images were trained with 10 trials and different train attempts for each trial, were then tested with 40 images. The result shows that the CNN model has succeeded in identifying images of sweet corn insects with 80% up to 95% prediction accuracy for images with no background.

A Recognition Method of Crop Diseases and Insect Pests Based on Transfer Learning and Convolution Neural Network

“…Using high-tech means such as image processing technology and pattern recognition technology to judge the damage status of CDIP, and then take corresponding disease control measures in time can reduce human and financial resources, which is conducive to the intelligent development of crop cultivation management [9]. Image processing and pattern recognition technology are widely used in agricultural robots, precision agriculture, and other fields, which can be used as a good reference for the research on the diagnosis and recognition of CDIP [10].…”