# A Study on Tomato Disease and Pest Detection Method
## 2 Related Works  
In recent years, with the development of machine learning and deep learning, the application of computer vision in agriculture has achieved remarkable results, especially in the field of plant disease recognition. Traditional object detection algorithms require researchers to manually design features and use machine learning algorithms to classify the extracted features. Representative feature descriptors include Haar features [12], Histograms of Oriented Gradients (HOG) features [13], and the Deformable Parts Model (DPM) [14]. However, these algorithms rely greatly on manually extracted features, resulting in poor generalization, low robustness, and high computational complexity. Compared with traditional methods, deep learning-based object detection algorithms improve detection speed and accuracy and have become a focus of research in pest and disease detection. [PERSON] et al. [15] proposed a method to identify tomato yellow leaf curl virus and tomato spotted wilt virus, achieving an average diagnostic accuracy of 90%. [PERSON] et al. [16] proposed a feature pyramid structure based on Faster R-CNN that fully exploits the features of each layer, enabling the detector to maintain high detection accuracy for small targets. [PERSON] et al. [17] proposed incorporating refined filter banks into deep neural networks to address the class imbalance of the tomato dataset. [PERSON] et al. [18] proposed a lightweight deep neural network-based plant disease detection method that reduces the model size and parameter set. [PERSON] et al. [19] proposed the use of the YOLOv2 algorithm for tomato disease detection. [PERSON] et al. [20] improved the ResNet model for pest and disease detection and increased the detection accuracy. [PERSON] et al. [21] proposed the use of ResNet 101 to determine the severity of early leaf blight in tomato. [PERSON] et al. [22] proposed a transfer learning-based Convolutional Neural Network (CNN) framework for tomato pest classification. [PERSON] et al. [23] used a deep learning method to extract features of tomato leaf diseases such as yellow leaf curl virus, bacterial spot, and late blight. [PERSON] et al. [24] proposed a tomato pest and disease detection algorithm based on the YOLOv3 convolutional neural network. [PERSON] et al. [25] proposed the YOLOv3-tiny-IRB algorithm, which improves the feature extraction network, ameliorates the gradient disappearance phenomenon caused by excessive depth of the network structure, and improves the detection accuracy of tomato pests and diseases under occlusion and overlap conditions in real natural environments. To address the problem of lost feature information for small targets during transmission. [PERSON] et al. [26] proposed an automatic identification and detection method for crop leaf diseases based on a fully convolutional-switchable normalized dual-path network (FC-SNDPN) to reduce the influence of complex backgrounds on the image identification of crop diseases and pests. Although the above studies have strongly demonstrated the effectiveness of CNN structures in the field of plant pest and disease recognition, these models inevitably have problems due to their large number of parameters and high computational complexity. Therefore, many studies have focused on the design of lightweight CNNs. [PERSON] et al. [27] used a MobileNet architecture based on depth-separable convolution constructed on public datasets for plant disease recognition. [PERSON] et al. [28] proposed the use of DenseNet-77 as a backbone network for CornerNet, which reduces the model parameters and improves accuracy. [PERSON] et al. [29] designed LightMixer, a lightweight tomato leaf disease recognition model, to improve the computational efficiency of the entire network architecture and reduce the loss of disease feature information. [PERSON] et al. [30] optimized the MobileNetV2 model using an augmented loss function approach for recognizing rice diseases and pests in complex contexts.