麻豆淫院

In many Asian regions, especially in China, agricultural fields are typically small, scattered, and lack clear boundaries, which complicates effective crop distribution and agricultural analysis using remote sensing technology.

Now, a research group from the Hefei Institutes of 麻豆淫院ical Science of the Chinese Academy of Sciences, has addressed this challenge with a novel dual-branch deep learning model (DBL).

This model is for crop-type mapping of irregular agricultural fields in Asia. "It tackles the challenge of crop-type mapping in most of Asia's planting plots," said Associate Prof. Xu Taosheng, who led the team. Results of the research were in Remote Sensing of Environment.

In this study, the researchers introduced a new deep learning model and time-series datasets and developed the dual-branch network for mapping crop types in time-series remote sensing images. The model consists of two branches: one that captures large-scale landscape patterns, and another that focuses on fine-grained details, such as subtle changes in crop growth over time. This combination allows the model to recognize crop types accurately, even in complex and disorganized fields.

The model is able to analyze both time and space, according to the researchers. "Crops grow and change over time, and the model tracks these changes," said Xu. The researchers created two new datasets (CF and JM) to reflect the characteristics of scattered farmlands, with plots of different sizes and shapes. The model can track crop growth over time, capturing the dynamic nature of agriculture.

This new model showed an overall accuracy of 97.7% and a 90.7% accuracy in identifying crop types and small fields. This proved that the model is highly adaptable and accurate for real-world use, especially in regions with fragmented farmland.

"Our finding can facilitate agricultural research in regions with similar planting patterns, particularly in some Asian areas using the time-series remote sensing analysis," said Xu.