Large-Scale Crop Image Recognition Based on ConvLSTM Spatiotemporal Feature Extraction and LCMST-Net Model

With the advancement of agricultural modernization, the role of large-scale crop image recognition in precision agriculture, pest and disease monitoring, and crop yield prediction has become increasingly significant. To enhance the accuracy and efficiency of large-scale crop image recognition, this study introduces coordinate attention mechanism and ASPP module to improve the ConvLSTM model. It further combines the Localized Convolutional Multi-Scale Temporal Network (LCMST-Net) model to efficiently classify and recognize the extracted spatiotemporal features. In the results, the improved ConvLSTM achieved a 0.979 accuracy and a 0.926 recall. The average classification accuracy of LCMST-Net was 0.982, significantly higher than the control model. LCMST-Net performed well in metrics including MSE and MAE, further validating its advantages in prediction accuracy and classification performance. Research has shown that improved ConvLSTM and LCMST-Net models have significant advantages in feature extraction and classification performance, especially when dealing with complex spatiotemporal features, they can more accurately identify different types of crops. This study contributes to the automation and intelligence of crop growth monitoring, improving agricultural production efficiency and resource utilization efficiency.