# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Crop Grading  
As living standards improve, people's demands for crop quality continue to rise. In agricultural processes, scientifically scoring and grading crops is a core means of enhancing industry efficiency [93]. By establishing a standardized evaluation system, it is possible to precisely guide production management, achieve the selection of high-quality varieties, and make intelligent decisions on harvesting timing based on maturity grading during the harvesting phase to reduce losses. In the processing phase, grading based on quality differences enables optimized processing to maximize returns [94]. In crop growth management, classifying crops by grade is a critical step in meeting the needs of consumers and the processing industry for high-quality crops (Figure 6). Manual visual inspections lack consistency, and even with standards in place, errors are prone to occur, and the process is labor-intensive [95]. Therefore, leveraging deep learning, computer vision, and image processing technologies (Table 2) to enhance the accuracy of grading systems is imperative. excitation attention module is embedded into the backbone network to enhance attention on the key features of apples. The balance between accuracy and efficiency is achieved through the collaborative optimization of three components. However, due to the imbalance in the dataset setup, with insufficient data on grade-3 apples, the model is prone to overfitting in detecting grade-3 apples. Ref. [104] aims to balance precision with model lightweighting. First, ref. [104] leverages target information from multiple perspectives to address the limitation of traditional methods, which rely on single-perspective images and cannot fully capture apple surface information. Then, by introducing lightweight convolutions and dynamic convolutions into the detection module, the feature extraction capability is enhanced while reducing computational load, thereby improving inference speed. Additionally, this study embeds the Swin Transformer module into the backbone network, utilizing a window self-attention mechanism to integrate global features and enhance grading accuracy. While achieving model lightweighting, grading accuracy is maintained. However, this study remains focused on apples as a single crop, and its generalization capabilities also have limitations. Compared to crops like apples, which have large fruits and dispersed targets, crops like tea and tobacco have leaf-based grading targets. Leaves are thin and prone to sticking together, with overlapping leaves and difficulty in determining maturity. Additionally, grading and classification for such crops are strict, with each grade corresponding to a specific price. For tea, traditional manual grading is inefficient and costly, and existing mechanical harvesting often mixes different grades of tea, affecting subsequent processing quality. Ref. [97] Based on the YOLOv8n model, the SPD-Conv module is used to enhance feature extraction capabilities for low-resolution and small-target objects like tea leaves. Additionally, the Super-Token Vision Transformer (SViT) is employed to reduce redundant information interference and improve perception of small targets. To better classify tea leaves, data is collected and enhanced to cover various lighting and weather conditions, with different grades of tea leaves annotated. This improved the accuracy of automatic tea grading. Similarly, to address the issues of low efficiency and strong subjectivity in traditional manual grading, [PERSON] et al. [98] proposed a method in which tobacco leaves are classified into different grades based on their local features. To capture the subtle local features of tobacco leaves, a dual-branch ensemble model was designed. The improved A-ResNet-65 network serves as the global branch to scale the entire image proportionally. The local branch uses the ResNet-34 network to crop local image blocks from high-resolution images. The results from the dual branches are integrated using a weighted voting method for feature fusion.