Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat head detection model founded on the streamlined YOLO v11n framework. The model optimizes performance through three key innovations: This study introduces a Global Edge Information Transfer (GEIT) module architecture that incorporates a Multi-Scale Edge Information Generator (MSEIG) to enhance the perception of wheat head contours through effective modeling of edge features and deep semantic fusion. Additionally, a C3k2_RFCAConv module is developed to improve spatial awareness and multi-scale feature representation by integrating receptive field augmentation and a coordinate attention mechanism. The utilization of the Normalized Gaussian Wasserstein Distance (NWD) as the localization loss function enhances regression stability for distant small targets. Experiments were, respectively, validated on the self-built multi-temporal wheat field image dataset and the GWHD2021 public dataset. Results showed that, while maintaining a lightweight design (3.6 MB, 10.3 GFLOPs), the YOLOv11n-GRN model achieved a precision, recall, and mAP@0.5 of 92.5%, 91.1%, and 95.7%, respectively, on the self-built dataset, and 91.6%, 89.7%, and 94.4%, respectively, on the GWHD2021 dataset. This fully demonstrates that the improvements can effectively enhance the model’s comprehensive detection performance for wheat ear targets in complex backgrounds. Meanwhile, this study offers an effective technical approach for wheat head detection and yield estimation in challenging field conditions, showcasing promising practical implications.

Wheat is a crucial grain crop in China, yet differentiating different wheat varieties at the mature stage solely through visual observation remains challenging. However, the automatic identification of wheat varieties at the mature stage is very important for field management, planting area, and yield prediction. In order to achieve accurate and efficient recognition of wheat varieties planted in wheat fields, a recognition method based on an enhanced DenseNet network model is proposed in this study. The incorporation of SE and ECA attention mechanisms enhances the feature representation capability, leading to improved model performance and the development of the SECA-L-DenseNet model for wheat variety recognition. The experimental results show that the SECA-L-DenseNet model achieves a classification accuracy of 97.15% on the custom dataset, surpassing the original DenseNet model by 2.13%, which demonstrates a significant improvement. The model enables the accurate identification of wheat varieties in the field and can be integrated into applications for the automated identification of varieties, planting area estimation, and yield prediction in harvester equipment.

Precision spraying is a crucial goal for modern agriculture to achieve water and fertilizer conservation, reduced pesticide use, high yield, and green and sustainable development. This relies on the accurate identification of crop positions, high-precision path planning, and the positioning and control of intelligent agricultural machinery. For the precision production of corn, this paper proposes a new row detection method based on histogram peak detection and sliding window search, avoiding the issues of deep learning methods that are not conducive to lightweight deployment and large-scale promotion. Firstly, green channel segmentation and morphological operations are performed on high-resolution drone images to extract regions of interest (ROIs). Then, the ROIs are converted to a top-view image using perspective transformation, and a histogram analysis is performed using the find_peaks function to detect multiple peaks corresponding to row positions. Furthermore, a sliding window centered around the peak is constructed to search for complete single-row crop pixels in the vertical direction. Finally, the least squares method is used to fit the row curve, estimating the average row spacing (RowGap) and plant spacing (PlantGap) separately. The experimental results show that the accuracy of row detection reaches 93.8% ± 2.1% (n = 60), with a recall rate of 91.5% ± 1.8% and an F1 score of 0.925 ± 0.018. Under different growth stages, row numbers (6–8 rows), and weed interference conditions, the average row spacing measurement error is better than ±2.5 cm, and the plant spacing error is less than ±3.0 cm. Through field verification, this method reduces pesticide use by 23.6% and water consumption by 21.4% compared to traditional uniform spraying, providing important parameter support for field precision planting quality assessment and the dynamic monitoring of planting density, achieving variable irrigation and fertilization and water resource conservation.