Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP_0.5, and mAP_0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection.

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases.

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents the bending and threshing characteristics of the actual rice plant. Initially, based on the characteristics of the rice plant, the rice stalk was segmented into three sections of hollow stalks with distinct structures—namely, the primary stalk, the secondary stalk, and the tertiary stalk—ensuring that the model closely resembles actual rice plants. Secondly, the mechanical and contact parameters for each structure of the rice plant were measured and calibrated through mechanical and contact tests. Subsequently, utilizing the Hertz–Mindlin contact model, a multi-dimensional element particle arrangement method was employed to establish a discrete element model of the entire rice plant. The bending characteristics of the stalk and the threshing characteristics of the rice were calibrated using three-point bending tests and impact threshing tests. The results indicated calibration errors in the bending resistance force of 4.46%, 3.95%, and 2.51% for the three-section stalk model, and the calibration error for the rice model’s threshing rate was 1.86%, which can accurately simulate the bending characteristics of the stalk and the threshing characteristics of the rice plant. Finally, the contact characteristics of the model were validated through a stack angle verification test, which revealed that the relative error of the stacking angle did not exceed 7.52%, confirming the accuracy of the contact characteristics of the rice plant model. The findings of this study provide foundational models and a theoretical basis for the simulation of and analytical applications related to rice threshing and cleaning.

Accurate detection and counting of rice ears serve as a critical indicator for yield estimation, but the complex conditions of paddy fields limit the efficiency and precision of traditional sampling methods. We propose REU-YOLO, a model specifically designed for UAV low-altitude remote sensing to collect images of rice ears, to address issues such as high-density and complex spatial distribution with occlusion in field scenes. Initially, we combine the Additive Block containing Convolutional Additive Self-attention (CAS) and Convolutional Gated Linear Unit (CGLU) to propose a novel module called Additive-CGLU-C2F (AC-C2f) as a replacement for the original C2f in YOLOv8. It can capture the contextual information between different regions of images and improve the feature extraction ability of the model, introduce the Dropblock strategy to reduce model overfitting, and replace the original SPPF module with the SPPFCSPC-G module to enhance feature representation and improve the capacity of the model to extract features across varying scales. We further propose a feature fusion network called Multi-branch Bidirectional Feature Pyramid Network (MBiFPN), which introduces a small object detection head and adjusts the head to focus more on small and medium-sized rice ear targets. By using adaptive average pooling and bidirectional weighted feature fusion, shallow and deep features are dynamically fused to enhance the robustness of the model. Finally, the Inner-PloU loss function is introduced to improve the adaptability of the model to rice ear morphology. In the self-developed dataset UAVR, REU-YOLO achieves a precision (P) of 90.76%, a recall (R) of 86.94%, an mAP_0.5 of 93.51%, and an mAP_0.5:0.95 of 78.45%, which are 4.22%, 3.76%, 4.85%, and 8.27% higher than the corresponding values obtained with YOLOv8 s, respectively. Furthermore, three public datasets, DRPD, MrMT, and GWHD, were used to perform a comprehensive evaluation of REU-YOLO. The results show that REU-YOLO indicates great generalization capabilities and more stable detection performance.

As one of the world’s most important staple crops providing food, feed, and industrial raw materials, corn requires precise kernel detection for seed phenotype analysis and seed quality examination. In order to achieve precise and rapid detection of corn seeds, this study proposes a lightweight corn seed kernel rapid detection model based on YOLOv11n (LWCD-YOLO). Firstly, a lightweight backbone feature extraction module is designed based on Partial Convolution (PConv) and an efficient multi-scale attention module (EMA), which reduces model complexity while maintaining model detection performance. Secondly, a cross layer multi-scale feature fusion module (MSFFM) is proposed to facilitate deep feature fusion of low-, medium-, and high-level features. Finally, we optimized the model using the WIOU bounding box loss function. Experiments were conducted on the collected Corn seed kernel detection dataset, and LWCD-YOLO only required 1.27 million (M) parameters and 3.5 G of FLOPs. Its precision (P), mean Average Precision at 0.50 (mAP_0.50), and mean Average Precision at 0.50:0.95 (mAP_0.50:0.95) reached 99.978%, 99.491%, and 99.262%, respectively. Compared to the original YOLOv11n, the model size, parameter count, and computational complexity were reduced by 50%, 51%, and 44%, respectively, and the FPS was improved by 94%. The detection performance, model complexity, and detection efficiency of LWCD-YOLO are superior to current mainstream object detection models, making it suitable for fast and precise detection of corn seeds. It can provide guarantees for achieving seed phenotype analysis and seed quality examination.

Traditional methods of pest control for sweet potatoes cause the waste of pesticides and land pollution, but the target detection algorithm based on deep learning can control the precise spraying of pesticides on sweet potato plants and prevent most pesticides from entering the land. Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. Then, cross-scale feature fusion and the general efficient aggregation architecture are used to further enhance the network feature extraction capability. Finally, the loss function is replaced with InnerFocaler-IoU (IFIoU) to improve the convergence speed and robustness of the model. Experimental results showed that the mAP0.5 and model size of the improved network reached 96.3% and 7.6 MB. Compared with the YOLOv8s baseline network, the number of parameters was reduced by 67.8%, the amount of computation was reduced by 53.1%, and the mAP0.5:0.95 increased by 3.5%. The improved algorithm has higher detection accuracy and a lower parameter and calculation amount. This method realizes the accurate detection of sweet potato plants in the natural environment and provides technical support and guidance for reducing pesticide waste and pesticide pollution.