Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks

Abstract

Feature extraction is a crucial part of advanced image recognition systems. In this research, an autonomous detection device was designed and developed for insect pest detection to improve the ability of intelligent systems in order to annihilate harmful insect pests in agricultural crop fields. Device included a dark chamber, a CCD digital camera, a LDR lightening module and a personal computer. The proposed programme for precise insect pest detection was based on an image processing algorithm and artificial neural networks (ANNs). After image acquisition, the insect pests’ images were extracted from original images with Canny filtration. Afterwards, four morphological and three textural features from the obtained images were measured and normalised. Performance of ANN model was tested successfully for Beet armyworm (Spodoptera exigua) recognition in images using back-propagation supervised learning method and inspection data. Results showed that proposed system was able to identify S. exigua in the images from other species. Such this machine vision system can be used in autonomous field robots to achieve a modern farmer’s assistant.