# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Disease Monitoring
Traditional features are difficult to accurately represent the target, and manual extraction is time-consuming and labor-intensive. Therefore, in [108], a dual-branch model combining convolutional neural networks and visual Transformers (ViTs) is proposed, abandoning traditional features for crop disease classification. Crop disease lesions are complex, requiring the integration of local and global details while considering contextual information. This study employs a dual-branch parallel approach to extract local and global features and dynamically fuse the two types of features. The Transformer branch uses Separable Self-Attention (SSA) to reduce computational overhead. This dynamically fused dual-branch structure achieves high-precision, low-cost crop disease classification. Pest control is another measure to ensure crop yield. Ref. [109] focuses on the detection of flying insects and small target insects. This study avoids the distortion issues caused by traditional models directly scaling images through a sliding window mechanism. The backbone network introduces EfficientNetV2-S to enhance feature extraction capabilities. Decoupling classification and localization tasks reduces task conflicts. These customized improvements significantly enhance the accuracy of small target pest detection. However, the model lacks instance segmentation or edge detection capabilities, and densely distributed pests are easily misidentified as single targets. No domain adaptation training was conducted for environments with shadows, reflections, or low light. In practical deployment, it can be used with mobile monitoring devices such as drones. Similarly, for small target pest detection, ref. [118] expanded the experimental scope to complex field environments. The system evaluated the performance of six YOLO variants in small target pest detection, deployed the optimized model on mobile devices through compression, and achieved efficient inference on mobile devices. Ref. [110] proposed a deep learning model named ResNet-SA. This model is based on residual network (ResNet) and self-attention mechanism (SA), enhancing the model's ability to extract key features. But the dataset used in (104) only contains 3150 original images, which were expanded through basic geometric transformations. The model proposed in the paper achieves an accuracy rate of 99.8% on the training set. However, in real-world scenarios, the morphology, posture, and lighting conditions of pests may be more complex. The model's performance on more complex datasets or in real agricultural environments may decline. Additionally, the risk of overfitting due to small datasets should not be overlooked. Therefore, the high performance (99.80%) on the test set in this paper still requires further validation through more complex real-world scenarios or cross-dataset validation. Ref. [111] combines the strengths of single-stage and two-stage detection networks to balance accuracy and speed. The single-stage network uses Inception for multi-scale feature extraction, improving detection of occluded targets. The two-stage network is based on Faster-RCNN. Clustering algorithms optimize anchor box sizes to enhance localization accuracy. This multi-scale approach gives the model clear advantages in detecting pests of varying sizes. The dataset uses overlapping annotations to help the model learn over lapping features, reducing missed detections. The method performs well even in complex field conditions with pest clusters or leaf occlusion.