MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas

Abstract

High-precision mapping of agricultural crops in complex planting areas is a prerequisite for precision agricultural management. This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. MSCPUnet is based on the Unet model and enhances performance through the incorporation of attention mechanisms, multi-scale pooling layers, and a multi-task learning approach for parallel processing. A ZiYuan-1 02D (ZY1E) satellite image collected from the He-Tao irrigation district in China is selected for plot extraction experiments, where MSCPUnet is compared with three other deep learning network variants. The Inter section over Union (IoU) and Accuracy metrics for the MSCPUnet model achieve the highest values, converging at 0.837 and 0.928, respectively. Leveraging this capability, a crop classification framework is proposed, which first extracts crop attributes from Sentinel-2 (S2) time series data. The plot information obtained from the MSCPUnet model is then combined with the area dominance method to assign crop attributes to vector plots, facilitating crop structure identification at the plot scale. Results indicate that this method significantly improves classification accuracy for fragmented farmlands, with overall accuracy (OA) rising to 91 % and Kappa coefficient increasing to 0.84 compared to a random forest classifier. This integrated crop classification approach has been validated in this study for high-precision crop mapping in complex planting areas.

Keywords

• Multi-task neural network
• Plot segmentation
• High-precision crop mapping
• ZY1E satellite