# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
###### Abstract  
(1) With the development of artificial intelligence, people expect to use modern information technology to solve the critical problems encountered in agriculture. How to identify sunflower diseases as early and quickly as possible and take corresponding measures has become a key issue for increasing crop production and farmers' income. Sunflowers, as an important oil crop, are vulnerable to infections by various diseases, such as downy mildew, leaf scar, gray mold, etc. (2) In order to select a better lightweight model that can be embedded into mobile devices or embedded devices for sunflower disease detection, we compared five lightweight deep learning models in this study, including SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small. The dataset used to train and test the models included 1892 images. These images were divided into four categories, namely, downy mildew, gray mold, leaf scar, and fresh leaves. (3) By evaluating the accuracy, precision, recall, and F1 score of each model, we found that EfficientNetV2-Small exhibited the highest performance with an accuracy of 90.19%. Whereas the other models, SqueezeNet, ShuffleNetV2, MnasNet-A1, and MobileNetV3-Small, achieved accuracies of 84.08%, 79.31%, 88.59%, and 84.08%, respectively. To address the problem of poor generalization ability of models caused by small datasets, we adopted the transfer learning technique. After doing that, the recognition accuracies of the five models, SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small, reached 96.02%, 95.23%, 94.96%, 96.92%, and 99.20%, respectively. The accuracies of these five models were improved by 14.2%, 20%, 7.2%, 15.2%, and 10%. Based on the comparative results, we found EfficeintNetV2-Small was an optimal choice for sunflower disease identification due to its high detection accuracy. artificial intelligence; image classification; lightweight deep learning model; sunflower disease identification; transfer learning 20251520142025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152025152015202515202  
yield reduction caused by crop diseases, thus increasing farmers' incomes. Furthermore, it can also reduce the use of pesticides and achieve green and sustainable development [6]. Sunflower, as a widely known oilseed crop plant, holds significant economic value and social significance globally [7]. Statistical data show that the global production of sunflower seeds in 2023/2024 was approximately 55 million metric tons [8]. In 2022/2023 the global production of sunflower seeds was approximately 53.4 million metric tons. In 2021/2022, it was approximately 46.3 million metric tons. From the trend of changes in global sunflower production in the world, it can be seen that it is showing an upward trend. This puts forward higher requirements for the accurate identification of plant diseases [9]. The diseases that affect the yield and quality of sunflowers mainly include downy mildew [10], gray mold [11], sclerotinia disease [12], leaf scar disease [13], and sunflower rust disease [14; 15]. Therefore, early and accurate identification of sunflower disease types followed by precise control measures can greatly reduce the impact of these diseases. The traditional approach to identify crop diseases primarily depends on farmers' long-term and meticulous observations, combined with their accumulated experience. This traditional method relies excessively on expert experience, so the accuracy of the diagnosis cannot be guaranteed. In precision agriculture, deep learning and Internet of Things (IoT) technologies continue to deeply integrate with agriculture [16]. In the pursuit of agricultural efficiency and productivity, people anticipate harnessing state-of-the-art information technologies to preemptively and precisely identify crop diseases in a timely manner. Concurrently, farmers harbor the expectation of effortlessly and autonomously diagnosing crop ailments.