# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Crop Identification and Detection  
Crop identification and localization play a crucial role in modern agriculture, as it is essential to accurately monitor crop growth conditions and locations throughout the agricultural process. In the past, crop identification and localization primarily relied on manual visual inspections in the field, which provided a direct means of monitoring crop growth status. However, the current labor shortage has led to increased costs associated with this method. With the development of computer technology and the widespread adoption of automation, manual crop identification monitoring is gradually being replaced, which helps alleviate labor shortages and better monitor real-time crop dynamics [29]. Crop identification can estimate crop density and locate crops. Density estimation aids in planning and managing planting, ensuring crops are planted at a reasonable density to increase yield. Location identification of crops can serve as guidance for automated harvesting, improving efficiency and saving human resources, and can also facilitate operations based on crop types [82; 83]. However, compared to manual inspection, computer vision faces various challenges during crop identification and detection, such as background interference, changes in lighting, and object occlusion (Figure 5). Therefore, a review of papers on crop identification and detection research is provided (Table 1). \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Target** & **Approach** & **Performance** & **Hardware Specifications** & **Number of Datasets** \\ \hline
[84] & Apple detection and localization & YOLOX, SPP & F1: 93\% & i7+RTX 2080 Ti (Intel, Senta Clara, CA, USA) & 4785 \\
[14] & Apple target recognition & YOLOv4, EfficientNet-B0, PANet & mAP50: 94.02\% & NVIDIA GTX 1080 Ti (Nvidia, Santa Clara, CA, USA) & 10,385 \\
[83] & Distribution density of strawberry fruits & Squeeze-and-Excitation, Kernel Density Estimation & mAP50-95: 87.3\% & NVIDIA GTX 1080 Ti (Nvidia, Santa Clara, CA, USA) & 4500 \\
[85] & Recognition of apple & SVM, BPNN, Watershed Algorithm & FNR: 4.65\% & NVIDIA GTX 1080 Ti (Nvidia, Santa Clara, CA, USA) & **UNYIA** \\
[86] & Tea detection & YOLOv4, DepC, Coordinate Attention, MobileNetV3 & Precision: 85.35\% & NVIDIA GTX 1080 Ti (Nvidia, Santa Clara, USA) & 4347 \\
[87] & Tomato detection & YOLOv10, D-LKA & mAP50: 91.8\% & NVIDIA GTX (Nvidia, Santa Clara, CA, USA) & 2000 \\
[88] & Tomato detection & YOLOv4-Tiny, CBAM & mAP50: 90.78\% & NVIDIA GTX (Nvidia, Santa Clara, CA, USA) & 8112 \\
[89] & Red pear small-target recognition & YOLOv9s, SCDown, C2 FUIBELAN & mAP50: 99.1\% & NVIDIA A16 (Nvidia, Santa Clara, CA, USA) & 1580 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the application of computer vision and deep learning in crop identification and detection. Figure 5: Apple inspection under occlusion, in bags, and at night. Detecting and identifying crops in complex environments has always been a challenge. As mentioned in [14], apples face issues such as leaf obstruction, dense fruit clusters, and insufficient lighting at night. Additionally, apples require packaging during their growth process, further complicating identification and localization. This study first replaces the native network of YOLOv4 with a lightweight network as the feature extraction network, significantly reducing the number of model parameters (the model size is only 29.8 MB, a 87.8% reduction from the original YOLOv4). Second, data augmentation was applied to increase background diversity and small object samples, enhancing the model's robustness in complex environments. Finally, spatial pyramid pooling and multi-layer feature fusion mechanisms were combined to enhance detection capabilities for small objects. While reducing model size and improving detection efficiency, the accuracy remained largely unchanged. Regarding improving detection accuracy, ref.