# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Crop Identification and Detection
[85] also addresses the challenge of identifying apples in complex backgrounds. This study converts RGB images to R-G grayscale to enhance fruit-background contrast. It segments images into irregular patches for classification, improving robustness to lighting variations and reducing bagging-induced misidentification. An optimized watershed algorithm boosts segmentation efficiency. The method achieves significantly higher accuracy with minimal preprocessing overhead--an acceptable trade-off for precision-critical applications. For crop identification and detection, it is not only necessary to perform individual object detection but also density estimation detection, which is particularly important. In [83], efforts were made to address the issue of missed detections caused by fruit occlusion and overlap, as well as to quantitatively analyze the spatial distribution of strawberries to guide reasonable planting density. This paper introduces the squeezed incentive attention mechanism and the Efficient Intersection over Union (EIOU) loss function into the YOLO module. The squeezed incentive attention mechanism enhances focus on strawberry features, while the EIOU loss function optimizes bounding box regression. This improves the model's detection accuracy in scenarios with fruit occlusion and overlap. The model uses Kernel Density Estimation (KDE) and nearest neighbor analysis (NNA) for density estimation, achieving the first end-to-end analysis from object detection to density distribution in strawberry images, providing more detailed spatial distribution information. The model outperforms YOLOv3-tiny in terms of accuracy. Additionally, the model detects high-density regions with clear boundaries that align with actual distributions. However, the morphology of crops changes at different stages, and density detection when fruits are small is not addressed. In [82], the proposed model can handle the diversity of different flowering periods and fruit maturity levels and addresses the detection of small-sized, densely distributed objects. This paper presents a U-Net model with an optimized ConvNeXt-T encoder, reducing parameters while maintaining performance. The model integrates density estimation and segmentation branches, using a Convolutional Block \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{4}{*}{[90]} & Recognition of & & Precision: 84.81\% & \multirow{4}{*}{NVIDIA GeForce RTX} & \multirow{4}{*}{1760} \\  & & YOLOv5s, RepVGG & & Recall: 85.64\% & \\ \cline{1-1}  & mango & & mAP50: 82.42\% & \multirow{4}{*}{Clara, CA, USA} & \multirow{4}{*}{100} \\ \cline{1-1}  & & VoVNet, & & & \\ \cline{1-1}  & & CenterNet2, & mAP50: 74.33\% & \multirow{4}{*}{NVIDIA RTX GTX 3090} & \multirow{4}{*}{2760} \\ \cline{1-1}  & detection & Location Guidance Module & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Attention Module (CBAM) to enhance target features and suppress background noise, improving robustness in complex farm environments. Random image flipping during training boosts generalization. However, higher input resolutions trade efficiency for accuracy, and insufficient labeled data may cause overfitting. The model is suitable for resource-limited devices (e.g., drones or smartphones) for large-scale orchard surveys. Different crop morphologies exhibit distinct characteristics. Ref. [86] achieves a balance between speed and accuracy through lightweight design and feature enhancement modules. Using images captured under various lighting conditions as datasets, it successfully enables all-weather tea bud detection. The model's nighttime performance is only 0.76% lower than under optimal lighting. However, its effectiveness is limited in occlusion scenarios as buds with over 75% occlusion were excluded during data annotation. Ref.