# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
## 5 Conclusions  
This study demonstrated the effectiveness of lightweight models in the classification and recognition of sunflower disease images, with EfficientNetV2-Small emerging as the superior model. The combination of compound scaling, optimized convolutional blocks, architecture search, and transfer learning enabled EfficientNetV2-Small to achieve high precision, recall, F1 score, and accuracy, making it a promising candidate for real-world applications in precision agriculture. The image dataset used in this study was relatively small in scale, covering only four types of sunflower diseases, and the number of images for each type was limited. Such limitations of the dataset may lead to the following problems:  
* Risk of overfitting: Due to the limited amount of data, the model may overfit the training data during the training process, resulting in poor performance on new, unseen data. This, to some extent, restricts the generalization ability of the model. * Lack of diversity: The insufficient diversity of the dataset may fail to cover all possible disease scenarios and environmental conditions, thereby affecting the robustness of the model in practical applications. * Insufficient representativeness: With a small number of images in the dataset, it may not fully represent the characteristics of sunflower diseases in different regions, seasons, and growth stages, leading to biases in the model's practical application. Moving forward, we plan to expand our research based on the findings of this study. One important direction is to expand the dataset by collecting additional images of sunflower diseases. The dataset used in this study was relatively small and not locally captured in China. To ensure the applicability of our research results in local areas, it is essential to gather more diverse and locally relevant image data. This will not only improve the robustness of our models but also enhance their ability to generalize to different environmental conditions. Another approach is to explore feature extraction methods to identify subtle differences in easily confused disease images. The confusion matrix we obtained showed that downy mildew and leaf scar were two diseases that were easily confused. Therefore, it is necessary to further explore the subtle features of the images to improve the accuracy of the model. Another avenue for future work is to develop applications and deploy them on mobile terminals or other embedded terminals, so as to achieve real-time image collection and recognition in the fields. This will enable farmers to perform real-time disease identification in the field by using these mobile terminals, facilitating timely intervention and management. We believe that through these continued efforts, the accuracy of sunflower disease identification can be further improved, ultimately contributing to reduce the impact of diseases on crop yields and increase economic benefits for farmers. This research also had limitations. During model training, the used images were sourced from abroad rather than collected in local areas. Therefore, the disease traits and characteristics may differ from those of sunflower diseases in local area. This could lead to inaccuracies in our model when classifying local sunflower diseases. Consequently, in order to promote the use of this model locally, it is necessary to collect a large number of images of various local sunflower diseases, fine-tune the model, and conduct in-depth training to obtain a lightweight deep-learning model that conforms to the characteristics of local sunflower diseases. This is also the direction of our future work. Conceptualization, methodology, software, writing--original draft preparation, and writing--review and editing, L.Z. ; validation, X.W. All authors have read and agreed to the published version of the manuscript. This research was supported by Interdisciplinary Research Fund of Inner Mongolia Agricultural University under Grant No. BR231506.