ECAENet: EfficientNet with efficient channel attention for plant species recognition

Abstract

It is an essential and challenging task to accurately identify unknown plants from images without professional knowledge due to the large intra-class variance and small inter-class variance. Aiming at the problem of low accuracy and model complexity, a lightweight plant species recognition algorithm using EfficientNet with Efficient Channel Attention (ECAENet) is proposed. The proposed approach is based on EfficientNet, which used neural architecture search to gain a baseline network and uniformly scales all dimensions of depth, width, and resolution using a compound coefficient. To overcome Squeeze-and-Excitation block complexity, the proposed method replaces all the two fully-connected layers in the channel attention modules with a fast one-dimensional convolution with an adaptive kernel, which avoids dimensionality reduction and effectively learns the discriminative features. The experimental results demonstrate that our ECAENet achieves 99.56%, 99.75%, 98.40%, and 93.79% accuracy on the well-known Swedish Leaf, Flavia Leaf, Oxford Flowers, and Leafsnap datasets, respectively. In particular, our method achieves 3.6x fewer network parameters and 8.4x FLOPs than others with similar accuracy. Therefore, our method achieves better recognition performance compared to most of the existing plant recognition methods.