Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requirin...Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requiring manual intervention or re-annotation for different seed types.To address these limitations,this paper proposes an automated method,LenRuler,with a primary focus on rice seeds and validation in multiple crops.The method leverages the Segment Anything Model(SAM)as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids,which are then fed into SAM for precise segmentation of the seed coat and radicle.The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length.Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944,demonstrating excellent segmentation performance.Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error(MAE)was 0.273 mm and the coefficient of determination(R^(2))was 0.982,confirming the method's high precision for rice.On the Otherseed dataset,the predicted radicle lengths for maize(Zea mays),pearl millet(Pennisetum glaucum),and rye(Secale cereale)are consistent with the observed radicle length distributions,demonstrating strong cross-species performance.These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice,with validated appli-cability to other crop species.展开更多
Climate change affects the heat and water resources required by agriculture, thus shifting cropping rotation and intensity. Shanghai is located in the Taihu Lake basin, a transition zone for various cropping systems. ...Climate change affects the heat and water resources required by agriculture, thus shifting cropping rotation and intensity. Shanghai is located in the Taihu Lake basin, a transition zone for various cropping systems. In the basin, moderate climate changes can cause major shifts in cropping intensity and rotation. In the present study, we integrated observational climate data, one regional climate model, land use maps, and agricultural statistics to analyze the relationship between heat resources and multi-cropping potential in Shanghai. The results of agro-climatic assessment showed that climate change over the past 50 years has significantly enhanced regional agro- climatic resources, rendering a shift from double cropping to triple cropping possible. However, a downward trend is evident in the actual multi-cropping index, caused principally by the increasing costs of farming and limitations in the supply of labor. We argue that improving the utilization rate of the enhanced agro-climatic resources is possible by introducing new combinations of cultivars, adopting more laborsaving technologies, and providing incentives to farmers.展开更多
基金This work was supported by the Yuelushan Laboratory Breeding Project(YLS-2025-ZY02006)to X.H.
文摘Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requiring manual intervention or re-annotation for different seed types.To address these limitations,this paper proposes an automated method,LenRuler,with a primary focus on rice seeds and validation in multiple crops.The method leverages the Segment Anything Model(SAM)as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids,which are then fed into SAM for precise segmentation of the seed coat and radicle.The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length.Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944,demonstrating excellent segmentation performance.Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error(MAE)was 0.273 mm and the coefficient of determination(R^(2))was 0.982,confirming the method's high precision for rice.On the Otherseed dataset,the predicted radicle lengths for maize(Zea mays),pearl millet(Pennisetum glaucum),and rye(Secale cereale)are consistent with the observed radicle length distributions,demonstrating strong cross-species performance.These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice,with validated appli-cability to other crop species.
文摘Climate change affects the heat and water resources required by agriculture, thus shifting cropping rotation and intensity. Shanghai is located in the Taihu Lake basin, a transition zone for various cropping systems. In the basin, moderate climate changes can cause major shifts in cropping intensity and rotation. In the present study, we integrated observational climate data, one regional climate model, land use maps, and agricultural statistics to analyze the relationship between heat resources and multi-cropping potential in Shanghai. The results of agro-climatic assessment showed that climate change over the past 50 years has significantly enhanced regional agro- climatic resources, rendering a shift from double cropping to triple cropping possible. However, a downward trend is evident in the actual multi-cropping index, caused principally by the increasing costs of farming and limitations in the supply of labor. We argue that improving the utilization rate of the enhanced agro-climatic resources is possible by introducing new combinations of cultivars, adopting more laborsaving technologies, and providing incentives to farmers.
