Traditional flat-bed rice nurseries in cold regions require large areas and suffer from inefficient tray movement and instability. A compact vertical rice seedling frame is developed in this work, which integrates a triangular-stabilized tray support, a four-point conveying mechanism, and anti-tilt/anti-torsion features, coupled with a dynamic lighting control strategy based on daily light integral (DLI). The mechanical design was validated by finite-element analysis under design loads, and collision-free operation was confirmed by digital mock-up simulation and prototype tests. In greenhouse trials comparing the vertical frame to conventional flat cultivation, the frame increased space-use efficiency to ΔP = 119.29% (i.e., ≈19.29% higher than the flat bench), while maintaining seedling coverage at 98%±1% (ns) and stem diameter (ns), and producing significantly shorter (more compact) seedlings (p<0.05; a/b). These results demonstrate a practical pathway for high-density automated seedling production in cold climates using intelligent vertical systems.

Rice is one of the most important staple crops in China, with an annual planting area of approximately 30 million hectares. However, the rice diseases have significantly impacted agricultural production, particularly in the regions with highly intensive farming and a high cropping index. The increasing prevalence of rice diseases has threatened the yield and food security. Early and accurate detection of rice diseases is often required for the effective control of rice diseases. However, several challenges still remain in the existing detection of embedded edge devices, such as the high computational demands of deep learning. This study aims to detect rice diseases using improved lightweight YOLOv8. Spot features of disease were extracted to enhance the detection accuracy in complex field environments. A diverse dataset of rice disease images was systematically collected from real-world fields. The image dataset also included the three major rice diseases: Rice Blast, Bacterial Blight, and Brown Spot. A strong foundation was provided to train and evaluate the deep learning models. In order to improve detection accuracy and computational efficiency, the lightweight model (YOLOv8-DiDL) was proposed to identify the rice disease using YOLOv8n. The key modifications were introduced to enhance the performance of the improved model. Firstly, an Inverted Residual Mobile Block (iRBM) was integrated into the backbone of convolutional modules. The micro disease features were then captured to promote the precision of detection on the small lesions. Secondly, a Deformable Convolutional Network (DCNv2) was incorporated to optimize the geometric size of disease symptoms. Stable performance of detection was achieved in complex and dynamic environments. Thirdly, a Dynamic Sample (DySample) operator was applied to reduce the computational complexity in real-world conditions. The model parameters were minimized for the computational overhead. The improved model was more efficient for the deployment of the resource-limited edge devices. Lastly, the standard Spatial Pyramid Pooling Fast (SPPF) module was replaced with a Large Separable Kernel Attention (LSKA) module. Multiple scale feature was fused in the pooling layer to recognize the diseases over the different scales and lighting conditions. A series of experiments were performed on the standardized platform. The results demonstrate that the improved YOLOv8-DiDL model was achieved with an accuracy of 91.4%, a recall of 83.5%, a mean average precision (mAP) of 90.8%, a parameter count of 2 270 553, and a model weight of only 7.5 MB. Compared with the baseline YOLOv8n network, the improvements were 7.0% in accuracy, 0.5% in recall, and 2.5% in mAP, while simultaneously the model weights were reduced by 9.7% and floating-point operations per second by 7.4%. A comparison was also made on the backbone network, heatmaps, and full-process feature maps. The high effectiveness was found after modifications. Each enhancement positively contributed to both detection accuracy and computational efficiency, thus enhancing the real-time detection of small disease spots. The improved model was achieved with high accuracy and lower computational costs, thus making it feasible for real-world agricultural applications. The reliability of the improved model was validated to detect the rice diseases. An advanced approach was then provided for the precision management in rice fields. Both detection accuracy and deployment efficiency were improved for real-time disease monitoring in intelligent agriculture. The finding can also be further extended into the detection of crop diseases in smart and sustainable agriculture.

Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R² = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.