Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.

Automated summary

The field of computer vision has great potential for large-scale crop identification using multispectral images, but there is a challenge in finding a balance between accuracy and lightweight design. This paper proposes an improved encoder-decoder framework based on DeepLab v3+ for accurate identification of crops with different planting patterns. The network incorporates ShuffleNet v2 as the backbone and a convolutional block attention mechanism in the decoder module. The results show significant improvements in performance compared to the original DeepLab v3+ on two datasets, DS1 and DS2, representing large-scale and scattered crop planting areas, respectively. The proposed Deep-agriNet is more efficient with fewer parameters and operations, making it an effective tool for crop identification in different regions and countries.