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.

At present, the picking of nectarines mainly relies on manual completion in China, and the process involves high labor intensity during picking and low picking efficiency. Therefore, it is necessary to introduce automated picking. To improve the accuracy of nectarine fruit recognition in complex environments and to increase the efficiency of automatic orchard-picking robots, a lightweight nectarine detection method, YOLOv8n-CSD, is proposed in this study. This model improves on YOLOv8n by first proposing a new structure, C2f-PC, to replace the C2f structure used in the original network, thus reducing the number of model parameters. Second, the SEAM is introduced to improve the model’s recognition of the occluded part. Finally, to realize real-time detection of nectarine fruits, the DySample Lightweight Dynamic Upsampling Module is introduced to save computational resources while effectively enhancing the model’s anti-interference ability. With a compact size of 4.7 MB, this model achieves 95.1% precision, 84.9% recall, and a mAP@0.5 of 93.2%—the model’s volume has been reduced while the evaluation metrics have all been improved over the baseline model. The study shows that the YOLOv8n-CSD model outperforms the current mainstream target detection models, and can recognize nectarines in different environments faster and more accurately, which lays the foundation for the field application of automatic picking technology.

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.

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.

Aiming at the challenges of potato storage scenarios, severe target stacking and occlusion, and large variations in defect characteristics in potato storage environments, a potato defect detection method named MDS-DETR was proposed, and its deployment on a robotic sorting platform was validated. First, potato images under different illumination conditions and stacking states were collected in potato storage warehouses to construct a potato defect dataset containing defects such as black spot, sprouting, dry rot, decay, green skin, and cracking. Subsequently, according to the characteristics of object detection tasks in storage scenarios, an MCF module was designed to strengthen the extraction and integration of contextual features across different spatial scales. An AIFI-DyMona structure, namely Anchor-free Instance Feature Interaction with Dynamic Mona, was constructed to improve the stability of feature representation under different illumination conditions. In addition, the Shape-IoU regression strategy was incorporated to improve the network sensitivity to irregular defect contours and geometric characteristics. Validation experiments demonstrated that the proposed MDS-DETR framework achieved 96.3% mAP@0.5, with only 14.2 M parameters and 42.9 G FLOPs. Compared with several representative object detection algorithms, the proposed method exhibited superior recognition capability and stronger robustness under complicated storage conditions, while also showing better suppression of missed and incorrect detections. To further evaluate its practical applicability, the trained network was integrated into an intelligent potato sorting robot and tested in real warehouse scenarios. Experimental observations indicated that the robotic system consistently maintained a sorting accuracy exceeding 95%, demonstrating the effectiveness and practical deployment potential of the proposed approach for potato storage applications. This study can provide a reference for intelligent detection and automated sorting in potato storage processes.