Weed management during the seedling stage is a critical component of soybean production. Efficient weed control can significantly improve crop yield and crop quality. However, conventional spraying techniques exhibit low pesticide utilization and contribute to environmental pollution. To address these challenges, this study proposes a deep learning-based precision target spraying method. A lightweight YOLOv5-MobileNetv3-SE model was developed by replacing the backbone feature extraction network and incorporating an attention mechanism. Field images of weeds were collected to construct a dedicated dataset, and the detection performance of the model was evaluated. Furthermore, a grid-based matching spraying algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control delay, and spray deposition delay, was analyzed and measured, and a time-delay compensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. The weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km·h⁻¹. Compared with conventional continuous spraying, the proposed method achieved pesticide reduction rates of 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practical approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact.

Distribution properties of airflow in the full envelope type maize stalk chopping chamber

Precise detection of maize root–stem junction is crucial for hole fertilization in maize cultivation. However, maize root–stem junction detection under field conditions is severely affected by soil clods, crop residues, and weeds, and is further complicated by variations in plant morphology, the small scale of targets, and their sparse spatial distribution. To address these issues, an improved model named PGi-YOLO is proposed in this study, based on YOLOv11n-OBB. A P2 high-resolution detection layer is introduced to improve multi-scale feature representation and enhance small-target localization. The C2PSA-iRMB module replaces the original attention module by integrating an inverted residual mobile block (iRMB) mechanism, thereby strengthening global contextual information fusion while preserving its lightweight design. In addition, the Group Shuffle Convolution (GSConv) module is adopted to replace part of the standard convolution operations, reducing computational redundancy and improving inference efficiency. Experimental results show that PGi-YOLO achieves a precision of 92.0%, a recall of 93.4%, and an mAP@0.5 of 96.9%, with parameters of 2.61 M, a model size of 6.0 MB and an inference time of 5.1 ms. Overall, PGi-YOLO achieves a favorable balance between accuracy and efficiency, demonstrating strong robustness for maize root–stem junction detection in complex field environments and providing reliable support for precision agriculture applications.

Selecting high-quality seeds is an effective approach for increasing wheat yields. Phenotype-based seed selection has emerged in recent years as a simple and convenient method. However, due to the irregular shape of seeds, accurately measuring shape parameters via imaging for seed viability assessment poses certain challenges. This study statistically analyzed the embryo morphology of wheat variety Bainong 207, identifying three predominant phenotypes. The morphological parameters of seeds and embryos were measured for these three phenotypes. Germination tests were conducted on these three categories of wheat in accordance with Chinese national standards. The seeds with Phenotype Ⅰ exhibited the highest germination force (89.33%) and the highest germination percentage (96.00%), representing a statistically significant difference from Phenotype Ⅱ and Ⅲ. Morphological parameters related to seed vigor, including germinative force, germination percentage, seedling height, and root length, were measured. By exploring the relationship between embryo phenotypes and wheat seed viability and yield potential, principles and considerations for wheat seed selection based on embryo phenotypes were discussed. The YOLOv8 model was employed to classify wheat seeds with different embryo phenotypes. Under the global labeling of seeds, the classification accuracy for the three categories reached 99.9%. Classification results from various labeling methods were compared, validating the feasibility of machine vision for seed selection and providing technical support for large-scale wheat seed improvement.

Weed management is a critical component of soybean production. Efficient weed con-trol can improve both yield and crop quality. However, conventional spraying tech-niques exhibit low pesticide utilization and contribute to environmental pollution. To address these issues, this study proposes a deep learning–based precision target spray-ing method. A lightweight YOLOv5-MobileNetv3-SE model was developed by modi-fying the backbone feature extraction network and incorporating an attention mecha-nism. Field images of weeds were collected to construct a dataset, and the detection performance of the model was subsequently evaluated. A grid-based matching spray-ing algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control de-lay, and spray deposition delay, was analyzed and measured, and a time-delay com-pensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. Experimental results showed that weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km/h. Compared with conventional continuous spraying, pesticide reduction rates reduced 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practi-cal approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact.