Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance.

Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep learning. The method encompasses the entire process from data preprocessing and model training to validation analysis and innovation optimization with the goal of deepening students’ understanding of AI technology and enhancing their ability to apply it to real-world scenarios. We have designed an experimental framework that incorporates the Efficient Multi-Scale Attention (EMA) mechanism into the Backbone network. This approach helps students understand the principles of feature extraction and the core functions of attention mechanisms. Additionally, we introduced a novel architecture—Convolution 3 Dilated Convolution X (C3DX)—into the Neck network. This architecture effectively expands the network’s receptive field, improves its ability to capture multi-scale information, and thus enhances defect detection accuracy. Furthermore, the implementation of the Efficient Intersection over Union (EIoU) loss function optimizes the bounding box predictions, further increasing the model’s accuracy and robustness. Overall, the teaching design not only ensures that the content remains at the cutting edge of technology but also emphasizes its practicality and operability. This approach enables students to effectively apply theoretical knowledge to real-world engineering projects.

The precise and automated diagnosis of apple leaf diseases is essential for maximizing apple yield and advancing agricultural development. Despite the widespread utilization of deep learning techniques, several challenges persist: (1) the presence of small disease spots on apple leaves poses difficulties for models to capture intricate features; (2) the high similarity among different types of apple leaf diseases complicates their differentiation; and (3) images with complex backgrounds often exhibit low contrast, thereby reducing classification accuracy. To tackle these challenges, we propose a three-residual fusion network known as MSCR-FuResNet (Fusion of Multi-scale Feature Extraction and Enhancements of Channels and Residual Blocks Net), which consists of three sub-networks: (1) enhancing detailed feature extraction through multi-scale feature extraction; (2) improving the discrimination of similar features by suppressing insignificant channels and pixels; and (3) increasing low-contrast feature extraction by modifying the activation function and residual blocks. The model was validated with a comprehensive dataset from public repositories, including Plant Village and Baidu Flying Paddle. Various data augmentation techniques were employed to address class imbalance. Experimental results demonstrate that the proposed model outperforms ResNet-50 with an accuracy of 97.27% on the constructed dataset, indicating significant advancements in apple leaf disease recognition.

In order to deepen students' understanding of the deep learning network development and application process, this paper designed a deep learning-based teaching experiment simulation method for C. oleifera diseases and pest segmentation. The experimental process includes training data collection and preprocessing, network model training, result verification and analysis, etc., throughout the whole process of deep learning-based image segmentation task development. To this end, CDM-DeeplabV3+ network was proposed to segment C. oleifera leaf diseases fast and accurately, which was composed of a Residual Attention ASPP(CBAM-ASPP) and Dual Attention Encoder (DAM-Encoder). CBAM-ASPP obtained multiscale information by extending to five dilated convolutions and focused on the edge features using Convolutional Block Attention Module. The DAM-Encoder structure, which was connected with CBAM-ASPP in parallel, highlighted the tiny features by combining global and local features. Then, the C. oleifera diseases and pest dataset was built, and the model was compared with the traditional segmentation models DeeplabV3+, UNet, HrNetV2 and PSPNet. The experimental results show that the mIoU of CDM-DeeplabV3+ reaches 85.63%, which is 5.82% higher than that of the original model and the model parameters are reduced by nearly five times, proving the effectiveness and feasibility of segmenting pests and diseases on C. oleifera. The design of the experimental teaching content is inspired by the scientific research experiments of artificial intelligence, following the frontier of the discipline and easy to realize, reflecting the teaching concept of the integration of science and education.