# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
###### Abstract
\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
**Author** & **Target** & **Models** & **Work Details** & **Results** \\ \hline [PERSON] et al., 2023 [20] & Chili & Squeeze-Net & The study explored the Squeeze-Net architecture and the impact of the different CNN optimizers and other training parameters on the identification accuracy. & The research results indicate that the Squeeze-Net CNN architecture can achieve a 100\% accuracy rate in classifying chili peppers when using the ADAM and RMSPROP optimizers. \\ \hline [PERSON] et al., 2023 [21] & Crop disease & ResNet-50 & Developing a smart web application using ResNet50 to identify crop disease. & Research shows that the accuracy of the ResNet-50 model is 98.98\%. \\ \hline [PERSON], et al., 2023 [22] & Maize leaf disease & Yolov5n & Adding CA attention module and STR to YOLOv5n. & The average recognition accuracy of the algorithm model can reach 95.2\%. \\ \hline [PERSON], et al., 2022 [23] & Cotton disease & CNN & Developing iOS app to make the disease predication. & The model accuracy is around 90\%. \\ \hline [PERSON], et al., 2021 [24] & Sunflower leaf disease & Yolov4 & Obtain three effective feature layers of MobileNet, and replace the feature layer of Yolov4 backbone. & The improved model has a certain improvement in evaluation of precision, recall, and F1. \\ \hline [PERSON] and [PERSON], 2021 [25] & Sunflower diseases & A hybrid model & Use the stacking ensemble learning technique and combine two models, which are VGG16 and MobileNet. & The proposed model gave 89.2\% accuracy on their dataset. \\ \hline [PERSON], et al., 2021 [26] & Cucumber leaf disease & Fine KNN & Using the feature extraction of HOG, LBP, and color features. & The best accuracy is 94.60\%. \\ \hline [PERSON] and [PERSON], 2020 [27] & Apple leaf disease & DenseNet-121 & Three methods of regression, multi-label classification, and focus loss function were proposed. & The accuracy of the three methods is 93.51\%, 93.31\%, and 93.71\%. \\ \hline [PERSON], et al., 2020 [28] & Rice leaf disease & 11 CNN models & 11 CNN models in transfer learning approach and in deep feature plus (SVM) were applied to rice disease classification. & The deep feature of ResNet50 plus SVM performs better with an F1 score of 98.38\% in rice disease identification. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._[PERSON] et al. [32] proposed a DFN-PSAN method based on YOLOv5, which achieved an accuracy of 94.47% on the sunflower dataset. The image information of the dataset used in the previously mentioned studies is shown in Table 2.  
From the analysis of the aforementioned literature, it can be observed that most implementations of deep learning for crop disease recognition are currently confined to the laboratory research phase. Some models always have a large number of parameters and require high hardware requirements, making them unsuitable for application on mobile devices to achieve real-time field image collection and recognition. In order to deploy deep learning models on mobile terminals, researchers have been making models more lightweight in aspects such as reducing model depth, decreasing model parameters, and lowering the hardware requirements of models. These kinds of model are called lightweight deep learning models. Many lightweight models are proposed, such as SqueezeNet [33], ShuffleNetV1 [34], ShuffleNetV2 [35], MnasNet-A1 [36], MobileNetV1 [37], MobileNetV2 [38], MobileNetV3 [39], EfficientNetV1 [40], EfficientNetV2 [41], etc. These lightweight models make it possible to run neural network models in mobile terminals or embedded devices. Researchers have indeed conducted relatively few studies on the field of identification of sunflower diseases, and even fewer have been put into practical application.