Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

State of charge (SOC) estimation is an increasingly important issue in battery management system (BMS) and has become a core factor to promote the development of electric vehicle (EV). In addition to offering the real time display of battery parameters to user, the accurate SOC information would exert some controls over the charging and discharging process that in turn reduces the risk of cell over voltage. Considering the shortcoming of traditional estimation methods and the harsh requirements in EV environment, a new method named combination algorithm is proposed in this paper in accordance with the characteristics of lithium-ion power battery. Some capacity effect factors, such as current rate and cell temperature, are also taken into consideration in the algorithm. The dynamic discharge test shows that the maximal SOC estimation error is less than 5%, which validates the feasibility and availability of the combination algorithm in the SOC estimation of electric vehicle.

Maize is one of the most important global crops. It is highly susceptible to diseases during its growth process, meaning that the timely detection and prevention of maize diseases is critically important. However, simple deep learning classification tasks do not allow for the accurate identification of multiple diseases present in a single leaf, and the existing RT-DETR (Real-Time Detection Transformer) detection methods suffer from issues such as excessive model parameters and inaccurate recognition of multi-scale features on maize leaves. The aim of this paper is to address these challenges by proposing an improved RT-DETR model. The model enhances the feature extraction capability by introducing a DAttention (Deformable Attention) module and optimizes the feature fusion process through the symmetry structure of spatial and channel in the SCConv (Spatial and Channel Reconstruction Convolution) module. In addition, the backbone network of the model is reconfigured, which effectively reduces the parameter size of the model and achieves a balanced symmetry between the model precision and the parameter count. Experimental results demonstrate that the proposed improved model achieves an mAP@0.5 of 92.0% and a detection precision of 89.2%, representing improvements of 7.3% and 8.4%, respectively, compared to the original RT-DETR model. Additionally, the model’s parameter size has been reduced by 18.9 M, leading to a substantial decrease in resource consumption during deployment and underscoring its extensive application potential.

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth’s key periods is feasible and effective.

Drought stress severely affects the normal growth and development of wheat, leading to reduced yields and quality. To address the lag and limitations of traditional drought monitoring methods, this paper proposes a drought stress monitoring model for winter wheat during critical growth stages based on deep learning. By acquiring drought stress images of winter wheat during three critical growth stages: rise-jointing, heading-flowering and flowering-maturity, a dataset correlating these images with soil moisture monitoring data was established. The DenseNet121 network model was selected as the base network to extract phenotypic features of winter wheat under drought stress. Variables such as the model's training approach, changes in learning rate, and whether an attention mechanism was included, were used to conduct eight sets of model training and optimization strategy experiments. A drought stress monitoring model for winter wheat during critical growth stages based on an improved DenseNet-121 was constructed. The results showed that the average recognition accuracy of the eighth group in the test set reached 94.67%, indicating good application prospects in the recognition of drought stress levels during critical growth stages of wheat.

Wheat is one of the major staple crops in China, and drought stress can severely affect its growth, development, and yield. Rapid and accurate identification of drought stress levels in wheat is of great significance for agricultural disaster prevention and mitigation, as well as for ensuring food security. To address the problems of insufficient fine-grained feature extraction, class imbalance, and unstable training in wheat drought stress image recognition, this study proposes an improved EfficientNet-B0 model for fine-grained wheat drought stress classification. Based on EfficientNet-B0, an improved lightweight Efficient Multi-scale Attention (EMA) module is introduced after the backbone network to enhance both channel-wise and spatial feature representation. PolyLoss is adopted to enhance the learning of low-confidence and difficult samples under the uneven class distribution, while the Sharpness-Aware Minimization (SAM) optimizer is employed to improve the optimization process. Experiments were conducted on a 15-class wheat drought stress image dataset constructed from three key growth stages and five drought severity levels. The proposed model achieved an accuracy of 98.69% and an F1-score of 98.37% on the internal test set, outperforming the baseline EfficientNet-B0 and comparison models including ResNet-50, DenseNet-121, and MobileNetV3. Component-level ablation experiments further showed that the dual-gating structure, projection residual connection, and intra-group Softmax normalization in the proposed EMA module all contributed positively to model performance. These results indicate that the proposed method provides a lightweight and effective approach for wheat drought stress recognition within the current dataset.