SenNet: A dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening

• A dual-branch image senescence segmentation model that integrates edge prior and local-global attention was proposed. • The model outperformed seven state-of-the-art models and achieves cross-year and cross-location transferability. • The senescence dynamics of varieties with different yields was revealed. • Novel traits of senescence dynamics were identified, enabling precise screening of high-yielding wheat varieties. Wheat is one of the three primary staple crops globally, with the senescence of its leaves having a direct effect on yield. However, conventional senescence evaluation methods are mainly based on visual scoring, which are subjective, time-consuming, and hamper the investigation of mechanisms between senescence process and yield formation. High-throughput image-based plant phenotyping techniques offer a promising approach. However, extracting senescence-related semantic information from images presents challenges, including blurred edge segmentation, inadequate characterization of senescence features, and interference from complex field environments. Therefore, this study proposes a dual-branch image senescence segmentation model ( SenNet ), which integrates edge priors and local–global attention mechanisms, including local–global hierarchical attention mechanisms, gated convolution, and positional encoding modules. First, a wheat senescence dynamics image dataset (19530 images) was constructed, comprising 509 wheat varieties from a two-year and two-replicate field experiments. Then, the SenNet model achieved senescence image segmentation for various wheat varieties, enabling senescence dynamics analysis and high-yielding variety screening. The results showed that: 1) The mean Intersection over Union (mIoU) of the SenNet model was 95.41 %, which represented a 4.01 % improvement over the average mIoU of seven state-of-the-art models. 2) The contributions of the local–global hierarchical attention mechanism, gated convolution, and positional encoding module to the accuracy improvement of SenNet were 3.15 %, 1.62 %, and 1.03 %, respectively. 3) SenNet can be transferred across years and locations. The mIoU accuracy of the SenNet across locations is 96.01 %. Furthermore, the model trained in 2023 can be transferred to 2022 and 2024, achieving mIoU accuracies of 93.75 % and 93.27 %. 4) High-yielding varieties typically experience a later onset of senescence and faster senescence in later stages. Based on the senescence law, this study further constructed new dynamic traits of senescence (e.g., AreaUnderCurve ). Leveraging the random forest-based yield prediction (R 2 = 0.68) from the dynamic traits, high-yielding varieties were screened with an average precision, recall, F1 score, and accuracy of 81 %, 79 %, 80 %, and 87 %, respectively. This study provides an efficient method for monitoring senescence dynamics and predicting yield, offering new insights into the screening of high-yielding varieties.