Pixel

MATLAB

Biology

Image processing

PEST analysis

Operating system

Classifier (UML)

Set (abstract data type)

Computer science

Botany

Code (set theory)

Insect pest

Pattern recognition (psychology)

Image (mathematics)

Agronomy

Artificial intelligence

Programming language

Convolutional neural network

Machine learning

Computer vision

Preprocessor

Ecological Informatics

Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing and create three different images for every saliency method. Hence, we create 3x3=9 new images for every original image to train different convolutional neural networks. We evaluate the performance of every preprocessing/network couple and we also evaluate the performance of their ensemble. We test our approach on both a small dataset and the large IP102 dataset. Our best ensembles reaches the state of the art accuracy on both the smaller dataset (92.43%) and the IP102 dataset (61.93%), approaching the performance of human experts on the smaller one. Besides, we share our MATLAB code at: https://github.com/LorisNanni/.

Insect pest image detection and recognition based on bio-inspired methods