# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
###### Abstract
They envision achieving this by simply capturing images of the affected areas of their crops using their mobile devices or other embedded systems, which would revolutionize the way they manage and protect their agricultural yields. In the field of crop identification, images of leaves, stems, and other parts of crops are used to identify the types of diseases and the severity. Some studies on crop disease research are shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Target** & **Models** & **Work Details** & **Results** \\ \hline \multirow{4}{*}{[PERSON], et al., 2024 [17]} & Rice leaf & DHLC- & Proposed a dense higher-level & The proposed model \\  & disease & DETR & composition feature pyramid & achieved an average \\  & & & network (DHLC-FPN) and & accuracy of 97.44\% on the \\  & & & integrated it into the detection & IDADP rice disease \\  & & & transformer (DETR) algorithm. & identification. \\ \hline \multirow{4}{*}{[PERSON] et al., 2019 [18]} & \multirow{4}{*}{Tomato} & \multirow{4}{*}{CNN model} & The proposed model comprises four \\  & & & convolutional layers, followed by \\ \cline{1-1} \cline{3-4}  & & & equivalent pooling layers. The \\ \cline{1-1} \cline{3-4}  & & & model also uses a sigmoid & impressively high \\ \cline{1-1} \cline{3-4}  & & & activation function and two dense \\ \cline{1-1} \cline{3-4}  & & & layers that are fully coupled. & accuracy rate of 87\%. \\ \hline \multirow{4}{*}{[PERSON] and [PERSON], 2023 [19]} & \multirow{4}{*}{Sunflower leaf disease} & \multirow{4}{*}{TeenyNet} & Designed a module to extract \\  & & & multi-frequency multi-scale features \\ \cline{1-1} \cline{3-4}  & & & and proposed lightweight \\ \cline{1-1} \cline{3-4}  & & & dual-fusion attention and \\ \cline{1-1} \cline{3-4}  & & & multi-branching structure to \\ \cline{1-1} \cline{3-4}  & & & identify sunflower disease. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies on crop disease identification using deep learning method. From Table 1, it can be seen that many researchers have applied deep learning methods to the disease identification of some crops or fruits, such as cotton, maize, rice, chili, tomato, cucumber, apple, sunflower, etc. In addition to the studies listed in Table 1, in terms of sunflower disease identification, [PERSON] et al. [29] proposed a hybrid model using transfer learning (TL) and CNN to detect sunflower diseases. The study utilized 467 images, including images of gray mold, downy mildew, leaf scar, and healthy leaves. The experiments demonstrated that the proposed hybrid model achieved the best results in terms of precision, recall, F1 score, and accuracy. [PERSON] et al. [30] developed a deep learning approach called SunNet for detecting sunflower diseases. The study investigated various disease classification techniques on 1428 sunflower leaf images and found that the best-performing classifier overall was a ResNet50 classifier, which achieved an average accuracy of 97.88%. [PERSON] et al. [31] compared the performance of five models in the identification of sunflower diseases, such as AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNetB3, and finally found that EfficientNetB3 had the highest accuracy of 97.9%.