文献类型： 外文期刊
作者： Zhang, Jun 1 ; Wu, Qiang 1 ; Duan, Fenghui 1 ; Feng, Mingzheng 2 ; Liu, Cuiping 1 ; Dai, Li 3 ; Wang, Xiaochun 4 ; Xiong, Shuping 1 ; Yang, Hao 5 ; Yang, Guijun 5 ; Chang, Shenglong 6 ; Ma, Xinming 1 ; Cheng, Jinpeng 1 ;
作者机构： 1.Henan Agr Univ, Coll Agron, Zhengzhou 450046, Peoples R China
2.Marine Design & Res Inst China, Shanghai 200011, Peoples R China
3.Henan Agr Univ, Grad Sch, Zhengzhou 450046, Peoples R China
4.Henan Agr Univ, Coll Life Sci, Zhengzhou 450002, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
6.Henan Normal Univ, Coll Software, Xinxiang 453000, Peoples R China
关键词： Unmanned aerial vehicle; State space models; Wheat lodging area identification; Semantic segmentation
期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：7.5； 五年影响因子：7.8 ）
ISSN： 0957-4174
年卷期： 2026 年 297 卷
页码：
收录情况： SCI
摘要： 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 F1score 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.
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