The intelligent transformation of crop leaf disease detection has driven the use of deep neural network algorithms to develop more accurate disease detection models. In resource-constrained environments, the deployment of crop leaf disease detection models on the cloud introduces challenges such as communication latency and privacy concerns. Edge AI devices offer lower communication latency and enhanced scalability. To achieve the efficient deployment of crop leaf disease detection models on edge AI devices, a dataset of 700 images depicting peanut leaf spot, scorch spot, and rust diseases was collected. The YOLOX-Tiny network was utilized to conduct deployment experiments with the peanut leaf disease detection model on the Jetson Nano B01. The experiments initially focused on three aspects of efficient deployment optimization: the fusion of rectified linear unit (ReLU) and convolution operations, the integration of Efficient Non-Maximum Suppression for TensorRT (EfficientNMS_TRT) to accelerate post-processing within the TensorRT model, and the conversion of model formats from number of samples, channels, height, width (NCHW) to number of samples, height, width, and channels (NHWC) in the TensorFlow Lite model. Additionally, experiments were conducted to compare the memory usage, power consumption, and inference latency between the two inference frameworks, as well as to evaluate the real-time video detection performance using DeepStream. The results demonstrate that the fusion of ReLU activation functions with convolution operations reduced the inference latency by 55.5% compared to the use of the Sigmoid linear unit (SiLU) activation alone. In the TensorRT model, the integration of the EfficientNMS_TRT module accelerated post-processing, leading to a reduction in the inference latency of 19.6% and an increase in the frames per second (FPS) of 20.4%. In the TensorFlow Lite model, conversion to the NHWC format decreased the model conversion time by 88.7% and reduced the inference latency by 32.3%. These three efficient deployment optimization methods effectively decreased the inference latency and enhanced the inference efficiency. Moreover, a comparison between the two frameworks revealed that TensorFlow Lite exhibited memory usage reductions of 15% to 20% and power consumption decreases of 15% to 25% compared to TensorRT. Additionally, TensorRT achieved inference latency reductions of 53.2% to 55.2% relative to TensorFlow Lite. Consequently, TensorRT is deemed suitable for tasks requiring strong real-time performance and low latency, whereas TensorFlow Lite is more appropriate for scenarios with constrained memory and power resources. Additionally, the integration of DeepStream and EfficientNMS_TRT was found to optimize memory and power utilization, thereby enhancing the speed of real-time video detection. A detection rate of 28.7 FPS was achieved at a resolution of 1280 × 720. These experiments validate the feasibility and advantages of deploying crop leaf disease detection models on edge AI devices.

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture.

Accurate identification of maize leaf diseases is essential for intelligent agricultural field management and targeted pesticide application. However, most current deep learning recognition research is limited to uniform laboratory backgrounds, presenting a significant domain gap with real-world farmland environments. Existing end-to-end classification models are prone to “Shortcut Learning” when facing complex field backgrounds containing soil, weeds, and varying illumination. They tend to fit environmental noise rather than true pathological characteristics, leading to poor generalization and the risk of illusory high performance. To fundamentally address this overfitting paradox, this paper proposes an innovative mask-guided pooling classification model requiring zero additional parameters (Mgp-YOLO11n). Specifically, we constructed a dataset of 3,092 real-world field images with complex backgrounds covering five maize health categories: Healthy, Gray Leaf Spot (GLS), Northern Leaf Blight (NLB), Northern Leaf Spot (NLS), and Curvularia Leaf Spot (CLS). We utilized a YOLO11-seg model embedded with a Convolutional Block Attention Module (CBAM) to achieve high-quality instance segmentation of disease spots. Subsequently, the traditional Global Average Pooling (GAP) layer is replaced in the downstream classification network. The prior mask serves as spatial hard attention weights, and a dynamic effective area normalization mechanism (MGP operator) is introduced during feature aggregation to achieve physical isolation at the feature space level. Classic large models were introduced as benchmarks for a “background retention” versus “black background” cross-validation, alongside deep interpretability analysis via Grad-CAM. Experimental results indicate that while the baseline YOLO11n-cls model achieves an initial accuracy of 92.30% in complex backgrounds, it decreases sharply to 77.30% in the black background test (in which pixels outside the annotated leaf mask are set to black while the leaf region and image dimensions remain unchanged—this masks rather than crops the background, eliminating background cues without forcing the model to attend specifically to lesions), confirming the defect of relying on background induced spurious correlations. In contrast, Mgp-YOLO11n improves accuracy in complex backgrounds to 93.24%, and its accuracy in black background tests increases to 95.94% with zero additional learnable parameters and negligible impact on real-time inference speed. Grad-CAM heatmaps conclusively demonstrate that the MGP mechanism successfully eliminates background shortcuts, overcomes the receptive field’s edge penetration issue, and forces the network to focus entirely on intrinsic pathological characteristics. This study reconstructs the feature extraction paradigm of the classification head, mitigates the accuracy-centric pitfall in agricultural vision, and provides a robust framework for deploying agricultural edge-intelligent terminals, such as plant protection drones, in highly complex farmland environments.

Six-dimensional (6D) pose estimation remains a significant challenge in computer vision, particularly for objects in complex environments. To overcome the limitations of existing methods in occluded and low-texture scenarios, a lightweight, multiscale feature fusion network was proposed. In the network, a self-attention mechanism is integrated with a multiscale point cloud feature extraction module, enhancing the representation of local features and mitigating information loss caused by occlusion. A lightweight image feature extraction module was also introduced to reduce the computational complexity while maintaining high precision in pose estimation. Ablation experiments on the LineMOD dataset validated the effectiveness of the two modules. The proposed network achieved 98.5% accuracy, contained 19.49 million parameters, and exhibited a processing speed of 31.8 frames per second (FPS). Comparative experiments on the LineMOD, Yale-CMU-Berkeley (YCB)-Video, and Occlusion LineMOD datasets demonstrated the superior performance of the proposed method. Specifically, the average nearest point distance (ADD-S) metric was improved by 4.2 percentage points over DenseFusion for LineMOD and by 0.6 percentage points for YCB-Video, with it reaching 63.4% on the Occlusion LineMOD dataset. In addition, inference speed comparisons showed that the proposed method outperforms most RGB-D-based methods. The results confirmed that the proposed method is both robust and efficient in handling occlusions and low-texture objects while also featuring a lightweight network design.