# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Weed Detection  
In addition to pests and diseases, weeds are also one of the key factors affecting agricultural yields. The widespread use of chemical herbicides places a significant burden on the ecological environment and undermines the sustainability of agricultural development [15]. Precise application of herbicides directly onto weeds can significantly reduce the use of chemicals, weed control costs, and environmental damage [119]. Some weeds resemble crops in appearance, while others overlap and shade crops (Figure 8). Therefore, accurately distinguishing between crops and weeds is crucial for precise herbicide application. A summary of relevant research is presented in Table 4. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Target** & **Approach** & **Performance** & **Hardware Specifications** & **Number of Datasets** \\ \hline \multirow{3}{*}{[119]} & Targeted & VGG-16, AlexNet, & Precision: 98\% & NVIDIA GeForce GTX & \\  & Weeds Control & GoogleNet & Recall: 97\% & 1080 GPU (Nvidia, F1: 97\% & Santa Clara, CA, USA) & 12443 \\  & & YOLOv5, HGNetV2, & & & \\  & & Scale Sequence & mAP50: 94.2\% & NVIDIA GeForce RTX & \\  & & Feature Fusion & Speed: 30.6 FPS & 3090 (Nvidia, Santa Clara, CA, USA) & 5270 \\  & & Module & & & \\  & & & mAP50: 90.8\% & NYIDIA GTX 3080 Ti & \\  & & YOLOv5, BiFPN, & Recall: 88.1\% & NYidia, Santa Clara, CA, USA) & 5000 \\  & & Swin Transformer & Precision: 64.4\% & NYidia, Santa Clara, CA, USA) & 5270 \\  & & Speed: 20.1 FPS & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the application of computer vision and deep learning in weed detection. Figure 8: Weed images from the CottonWeedDet12 dataset. Weed target sizes vary widely, and detecting small targets is challenging. Factors such as lighting and occlusion in complex farmland environments also affect detection performance. Therefore, ref. [120] proposed HAD-YOLO based on YOLOv5 to address the challenge of precise weed detection in farmland. This paper adopts the lightweight HGNetV2 architecture, combined with Deep Separable Convolution (DWConv) to reduce parameter counts and enhance feature extraction capabilities. The Scale Sequence Feature Fusion (SSFF) module and Triple Feature Encoding (TFE) module enhance the model's ability to process features of different scales. Finally, the Integrated Dynamic Attention Mechanism (Dyhead) is used to improve classification and localization accuracy. This strategy of multi-scale fusion and lightweight modeling enables the method to achieve good accuracy on both greenhouse and field datasets. However, its performance is average under occlusion conditions. Ref. [121] also based their research on the YOLO5 model for precise weed detection in rapeseed fields. This study faced the challenge of varying weed sizes (especially small targets) while also addressing the difficulty of distinguishing weeds from similar-looking rapeseed seedlings. Unlike [120,121] incorporates a Swin Transformer encoding module into the model to enhance global feature extraction while reducing computational complexity. The study employs a Bidirectional Feature Pyramid Network (BiFPN) structure for weighted multi-scale feature fusion. During the feature fusion process, the weights of different features are assigned by the Normalization-based Attention Module (NAM) attention mechanism, enabling adaptive fusion of multi-scale features. The introduction of Swin Transformer allows the module to better capture global contextual information, demonstrating certain improvements in addressing occlusion issues. This approach effectively balances computational efficiency with feature representation capability. A comparison of these two methods shows that the model in [120] is more lightweight and suitable for embedded deployment. However, it involves few weed species in its design and has insufficient generalization. It is suitable for real-time single weed detection tasks. Ref.