# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Disease Monitoring
However, some diseases do not have obvious symptoms, and by the time obvious symptoms appear, the plants have already suffered serious damage [106]. Computer vision based on deep learning can efficiently detect and classify plant diseases. In [115], researchers extracted the circumscribed rectangle aspect ratio (CRAR) feature and the Histogram of Oriented Gradients (HOGs) feature to distinguish morphological differences between diseased and healthy rice plants. Subsequently, a convolutional neural network was used for feature fusion to improve recognition accuracy. The paper designed two modes--offline and online--to work in tandem, adapting to poor network environments. This achieved the goal of rapid field detection of rice black stem disease. Similarly, ref. [116] also proposed a rapid detection method for black stem rust. However, both papers did not address multi-scale feature fusion, which is suboptimal for small object detection. In response, ref. [107] proposed YOLOv8-GDCI to address the real-time precise detection of PhytophthoraM bright on different parts of chili plants. This study employs the RepGFPN feature fusion network for parallel processing and cross-level feature fusion, enhancing the expression capability of multi-scale features. The dynamic upsampling algorithm expands the receptive field. Coordinate attention (CoordAtt) is introduced into the backbone network, combining channel and spatial position information to enhance the ability to capture key features of diseases. This mechanism effectively captures dense lesions and adapts to dense lesion scenarios. For the dataset used in the experiment, there are far more leaf labels than stem labels, which may result in the model having weaker detection capabilities for stem lesions. Additionally, the experimental data was collected under relatively uniform lighting conditions, and results may vary in real-world environments such as cloudy days or nighttime. Therefore, when deploying in high-resource scenarios, the full model can be used with high-resolution images to balance speed and accuracy. When using edge devices such as handheld terminals, model lightweighting can be performed to sacrifice a small amount of  
\begin{table}
\begin{tabular}{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
[109] & Detection of pests & YOLOv4, Fully CNN & mAP50: 84.22\% Speed: 4.72 FPS & 2\(\times\)GeForce GTX 1080 Ti (Nvidia, Santa Clara, CA, USA) & 3557 \\
[110] & Pest identification & ResNet, Self-Attention & Accuracy: 99.80\% F1: 99.33\% & Google Colab Pratform ([[https://colab.google/](https://colab.google/)]([https://colab.google/](https://colab.google/))) NVIDIA RTX 3080 (Nvidia, Santa Clara, CA, USA) & 4500 \\
[111] & Plant pest and disease detection & YOLOv3, Faster R-CNN, Inception & mAP50: 85.23 FPS & NVIDIA RTX 3080 (Nvidia, Santa Clara, CA, USA) & 26,106 \\
[112] & Disease classification & CNN, GAN, LSTM & Bacterial Blight: Precision: 96\% Recall: 97\% F1: 99\% F1: 99\% F1: 99\% F1: 99\% F1: 99  
accuracy for real-time performance. For extreme lighting conditions, infrared sensors or similar devices can be used in conjunction. As plant species and cultivation techniques continue to evolve, the number of plant diseases is also increasing. Different measures should be taken to address different plant diseases [117]. Therefore, classifying and accurately identifying plant diseases can effectively ensure crop health. Ref. [117] combines features extracted using deep learning with traditional features for multi-class classification of plant leaf diseases. A deep separable convolutional network with fewer parameters is used to extract high-level hidden features. The Local Binary Pattern (LBP) captures local texture information from leaf images. These two types of features are directly concatenated and classified using a Softmax classifier.