Lightweight Convolution Neural Network Based on Multi-Scale Parallel Fusion for Weed Identification

Abstract

Accurate identification of weed species is the premise for controlling weeds in field. But it is a challenging task due to the complexity and high-dimensional nonlinearity of the weed images in natural field. Convolutional neural networks (CNNs) model has been widely applied to image identification, but most of the CNNs models have the problems of large parameters, low identification accuracy, and single feature scale. This paper presents a novel deep neural network structure, named as MPF-Net for weed species identification. In MPF-Net, firstly, the weed images is sent into two different scales of depthwise separable convolution layers; secondly, the parallel output feature information is cross-fused, and uses the residual learning structure to increase the network model depth and feature extraction ability; finally the lightweight model PL-Model and the scale reduction module SR-Model are stacked together to construct the lightweight network. We have performed extensive experiments on real weed datasets, and compared the proposed MPF-Net against several variations of lightweight networks. The experimental results on the weed image dataset show that the proposed method is effective and feasible for weed species identification.