Walnut detection in mountainous and hilly regions often faces significant challenges due to obstructions, which adversely affect model performance. To address this issue, we collected a dataset comprising 2379 walnut images from these regions, with detailed annotations for both obstructed and non-obstructed walnuts. Based on this dataset, we propose OW-YOLO, a lightweight object detection model specifically designed for detecting small, obstructed walnuts. The model’s backbone was restructured with the integration of the DWR-DRB (Dilated Weighted Residual-Dilated Residual Block) module. To enhance efficiency and multi-scale feature fusion, we incorporated the HSFPN (High-Level Screening Feature Pyramid Network) and redesigned the detection head by replacing the original head with the more efficient LADH detection head while removing the head processing 32 × 32 feature maps. These improvements effectively reduced model complexity and significantly enhanced detection accuracy for obstructed walnuts. Experiments were conducted using the PyTorch framework on an NVIDIA GeForce RTX 4060 Ti GPU. The results demonstrate that OW-YOLO outperforms other models, achieving an mAP@0.5 (mean average precision) of 83.6%, mAP@[0.5:0.95] of 53.7%, and an F1 score of 77.9%. Additionally, the model’s parameter count decreased by 49.2%, weight file size was reduced by 48.1%, and computational load dropped by 37.3%, effectively mitigating the impact of obstruction on detection accuracy. These findings provide robust support for the future development of walnut agriculture and lay a solid foundation for the broader adoption of intelligent agriculture.

Accurate walnut yield prediction is crucial for the development of the walnut industry. Traditional manual counting methods are limited by labor and time costs, leading to inaccurate walnut quantity assessments. In this paper, we propose a walnut detection method based on UAV (UAV means Unmanned Aerial Vehicle) remote sensing imagery to improve the walnut yield prediction accuracy. Based on the YOLOv11 network, we propose several improvements to enhance the multi-scale object detection capability while achieving a more lightweight model structure. Specifically, we reconstruct the feature fusion network with a hierarchical scale-based feature pyramid structure and implement lightweight improvements to the feature extraction component. These modifications result in the RSWD-YOLO network (RSWD means remote sensing walnut detection; YOLO means ‘You Only Look Once’, and it is the specific abbreviation used for a series of object detection algorithms), which is specifically designed for walnut detection. Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. Through model deployment and testing on small edge devices, we demonstrate the feasibility of our proposed method. The detection algorithm achieves 86.1% mean Average Precision on the walnut dataset while maintaining operational functionality on small edge devices. The experimental results demonstrate that our proposed UAV remote sensing-based walnut detection method has a significant practical application value and can provide valuable insights for future research in related fields.

Field-scale pest monitoring requires accurate pest recognition and classification techniques. However, there are two main challenges in practical pest detection tasks. First, both intra-species morphological variation across developmental stages and inter-species size differences create challenges for models adapting to multi-scale features. Second, biological camouflage reduces target-background contrast, increasing the difficulty of model recognition. To address these issues, this paper proposes an improved pest detection model, IP-YOLOv8, based on YOLOv8s. First, a multi-scale feature fusion architecture is introduced, establishing a cross-layer feature interaction mechanism that effectively integrates shallow detailed features and deep semantic features, significantly enhancing the model’s multi-scale representation ability. Second, a dynamic detection head is designed to address the diverse morphology of pests. This head adapts the receptive field through a dynamic sampling mechanism, allowing the model to accurately capture pest features of varying scales and shapes. Finally, to tackle the issue of camouflage background confusion, an edge feature fusion module is proposed to enhance target contour information, thereby addressing the blurring of edge features caused by camouflage. Experimental results demonstrate that IP-YOLOv8 outperforms YOLOv8s on the IP102 dataset, achieving improvements of 2.2% in mAP50, 1.3% in mAP50:95, 3.1% in precision, and 1.5% in recall. This method effectively adapts to complex field pest detection tasks, providing strong technical support for precision agriculture.