UssNet: a spatial self-awareness algorithm for wheat lodging area detection

Abstract

Crop lodging significantly impacts wheat yield and quality, necessitating rapid and accurate identification methods for post-disaster response and agricultural insurance assessment. While extreme weather events causing lodging have increased in frequency, conventional semantic segmentation approaches face limitations in global context perception. This study introduces UssNet, an innovative algorithm integrating contextual and spatial awareness by combining UNet’s semantic segmentation strengths with State Space Models (SSM). We selected UNet as our base architecture due to its proven effectiveness with limited training data, encoder-decoder structure with skip connections that preserve critical spatial information for lodging detection, and optimal balance between computational complexity and performance. UssNet implements local auxiliary mechanisms with SSM, enabling selective scanning of feature maps from multiple directions to achieve linear computation of long sequences and comprehensive extraction of global contextual information. To address class imbalance challenges and improve recognition of small lodging areas, we incorporate the Focal Loss function. Additionally, we replace ReLu with GeLu activation to mitigate the “dead ReLu” phenomenon while maintaining overfitting suppression benefits. Experimental results demonstrate UssNet’s superior performance, achieving a pixel accuracy (PA) of 0.971, mean intersection over union (mIoU) of 0.931, recall of 0.85, and F1_score of 0.82 on the test dataset. Comparative analysis against state-of-the-art models confirms UssNet’s enhanced capability in capturing global context information, providing an efficient approach for wheat lodging monitoring with valuable applications in yield estimation and disaster management.