# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 4 Conclusions  
In this study, we present a new model to solve the difficult tasks of real-time detection in maize seedling images. The new model improves the accuracy of detection and reduces the number of parameters. By using the LW-YOLOv7 detection model, we can obtain the position and quantity of maize seedlings, as well as judge the density and growth uniformity of maize. In addition, we can calculate the emergence rate and replant the missed areas in a timely manner to increase maize yield. It is important for evaluating maize growth and yield. On the training model, we can change the depth and width of the network to reducing the size of weight. This provides a theoretical basis for deploying our improved model on devices with limited mobile resources. The proposed maize seedling detection model can provide benefits for scientific researchers engaged in object detection and for those who use the model for real-time detection in agriculture. Using this model can save a lot of manpower and time. In addition, this model can be integrated into unmanned aerial vehicle (UAV) systems for real-time detection, further enhancing its practicality. In this way, we can quickly and easily monitor large areas of land, identify seedlings that require attention, and take action before problems arise.  
While the proposed maize seedling model has many benefits, there are still challenges that need to be addressed to improve its generalizability across different datasets and environments. Firstly, the dataset has certain specificity. The model is trained and tested on a specific dataset, which may limit its adaptability to maize seedlings at different stages. If applied to scenarios with significant differences from the training dataset, the model's performance may decline. Secondly, the model has implicit class limitations. LW-YOLOv7 is a single-stage detector that typically predicts multiple bounding boxes and corresponding classes for each position in the input image. However, this design may result in implicit class limitations, meaning the model can struggle to accurately differentiate objects with overlapping or similar features. Finally, it involves model complexity and computational resource requirements. Despite having a relatively smaller model size compared to other popular object-detection models, LW-YOLOv7 may still require substantial computational resources. On low-performance devices such as mobile or embedded systems, the model may face challenges due to limited computational resources.  
Despite these challenges, our findings demonstrate that LW-YOLOv7 object-detection models offer great promise in addressing real-world problems. In the future, we plan to improve the detection accuracy and speed in maize seedling detection. The weight of the model further reduced to facilitate better deployment of identification tasks on the edge computing platform.  
Conceptualization, L.Z.; methodology, Y.Z.; writing--original draft preparation, K.Z.; project administration, H.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Supported by National Natural Science Foundation of China (Grant No. 31901399); Sub project of the Fourteenth-Five Year National Key R&D Plan (Grant No. 2021 YFD1500204); Sub project of National Key R&D Plan (Grant No. 2022 YFD2002303-01); Liaoning Province Innovation Capability Enhancement Joint Fund Project (Grant No. 2021-NLTS-11-03).  
Not applicable.  
The authors declare no conflict of interest.