Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately. The improved network model consists of three parts: feature extraction, attention calculation, and classification. The dataset used was first diversified through data augmentation operations such as random masking to enhance network robustness. An attention module was then used to generate feature maps at various depths. This increased the network’s focus on discriminative features, reduced background noise, and enabled the use of the LeakyReLu activation function in the attention module to prevent situations in which neurons fail to learn when the input is negative. Finally, the extracted features were then integrated using a fully connected layer, and the predicted disease category inferred to improve the classification accuracy of soybean leaf diseases. The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the improved method.The model proposed in this paper was also tested on the grapevine leaf dataset, and the performance ability of the improved network model remained due to other common network models, and overall the proposed network model was very effective in leaf disease identification.

Maize diseases have a great impact on agricultural productivity, making the classification of maize diseases a popular research area. Despite notable advancements in maize disease classification achieved via deep learning techniques, challenges such as low accuracy and identification difficulties still persist. To address these issues, this study introduced a convolutional neural network model named Sim-ConvNeXt, which incorporated a parameter-free SimAM attention module. The integration of this attention mechanism enhanced the ability of the downsample module to extract essential features of maize diseases, thereby improving classification accuracy. Moreover, transfer learning was employed to expedite model training and improve the classification performance. To evaluate the efficacy of the proposed model, a publicly accessible dataset with eight different types of maize diseases was utilized. Through the application of data augmentation techniques, including image resizing, hue, cropping, rotation, and edge padding, the dataset was expanded to comprise 17,670 images. Subsequently, a comparative analysis was conducted between the improved model and other models, wherein the approach demonstrated an accuracy rate of 95.2%. Notably, this performance represented a 1.2% enhancement over the ConvNeXt model and a 1.5% improvement over the advanced Swin Transformer model. Furthermore, the precision, recall, and F1 scores of the improved model demonstrated respective increases of 1.5% in each metric compared to the ConvNeXt model. Notably, using the Flask framework, a website for maize disease classification was developed, enabling accurate prediction of uploaded maize disease images.

The morphological characteristics of retinal vessels in the fundus serve as the primary basis for diagnosing and assessing the risk of ophthalmic diseases. An effective segmentation scheme for retinal vessels can aid in the early diagnosis and treatment of these diseases, as well as help prevent their progression. However, accurate vessel segmentation is challenging due to the low contrast of fundus images and the complexity of the vessels’ morphological structure. To address the low sensitivity and poor generalization ability of the existing methods in vascular extraction, a Dual-decoder Network based on Attention-enhancing and multi-scale Feature Fusion (AFFD-Net) is proposed. AFFD-Net inherits the codec concept of U-Net. To improve the performance of our U-Net model, we made two modifications. Firstly, we reduced the number of convolution kernel filters in each layer, thereby significantly reducing the number of training parameters. This helps to avoid overfitting and improves the model’s ability to generalize. Secondly, we added a Multi-scale Feature Extraction (MFE) module and an M/A intermediate decoder to enhance the model’s sensitivity. MFE is designed as the first encoding unit of AFFD-Net to obtain rich vascular features in the complex anatomical background and adapt to the large-scale variations of vessels. The M/A intermediate decoder is composed of the Multi-scale Feature Fusion (MFF) module and the Attention-enhancing Hybrid Feature Fusion (AHFF) module. The MFF module integrates deep semantic information and shallow spatial information to ensure that the features at each scale in the middle layer are fully utilized. The AHFF module adaptively fuses the hybrid features at different scales to generate two feature descriptors with different focuses which can improve the expressiveness of the model. AFFD-Net is evaluated on three public databases including DRIVE, STARE, and CHASE_DB1, and the sensitivity values obtained are 84.19%, 84.58%, and 82.62%, respectively. It has higher sensitivity and better generalization ability than other state-of-the-art methods. Compared with classical networks including U-Net, U-Net++, and U-Net3+, AFFD-Net has fewer parameters and higher segmentation accuracy. Our proposed segmentation model exhibits superior performance across a range of metrics, indicating its promising potential for practical applications.