GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases

Improved EfficientNet for corn disease identification

IntroductionCorn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.MethodsTo address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.ResultsCompared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.DiscussionOverall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control

A case of primary oncocytic adenocarcinoma of the lacrimal sac

Lacrimal sac tumours are rare entities. Patients often present with epiphora, recurrent dacryocystitis, and/or a lacrimal sac mass. Neoplasms of the lacrimal system may conveniently be grouped into epithelial and non-epithelial types: papillomas are the most common benign epithelial tumours, while oncocytic adenocarcinomas are extremely rare. Here we report a case of primary oncocytic adenocarcinoma of the lacrimal sac in a 56-year-old man

Fracture Modeling of Deep Tight Sandstone Fault-Fracture Reservoir Based on Geological Model and Seismic Attributes: A Case Study on Xu 2 Member in Western Sichuan Depression, Sichuan Basin

With significant geological reserves and high resource abundance, the Xujiahe Formation in the Western Sichuan Depression is considered a key target for natural gas exploration and development in continental clastic rocks within the Sichuan Basin. However, this formation remains underdeveloped. Critical to forming “sweet spots” of tight reservoir is the presence of fractures. Based on available data sources, including core samples, well logs, and outcrop data, we utilized a combination of geophysical and geological modeling techniques to clarify the characteristics of effective fractures in tight gas reservoirs. This allowed us to construct a geological model of a tight sandstone fault-fracture gas reservoir in the Xu 2 Member of the Xujiahe Formation located in the Xinchang area, which represents a fault-fracture reservoir formed by high-angle faulting-derived fractures and controlled by the S-N trending fault. With this model, a variety of seismic attributes, including likelihood and entropy, was used to predict the fault-fracture reservoir. Furthermore, geological information, well logs, and seismic attributes were integrated for characterizing the fractures of different scales. The cutoff on various attributes for characterizing the fault-fracture reservoir was defined, and the distribution of the fault-fracture reservoir was delineated. By using the geological modeling technique, the fracture model of the fault-fracture reservoir comprising natural fractures at different scales was built. This model provides further guidance for the exploration and development of the Xu 2 Member tight gas reservoirs in the Xinchang area and, as demonstrated by drilling results, has achieved remarkable effects in practice. This approach has shown good performance in characterizing fracture models. However, due to the complexity of fractures and the discrepancy between the scale of fractures and the scale that can be predicted by geophysical methods, there may still be some uncertainties associated with this method

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification

Design and Implementation of an Efficient Hardware Coprocessor IP Core for Multi-axis Servo Control Based on Universal SoC

The multi-axis servo control system has been extensively used in industrial control. However, the applications of traditional MCU and DSP chips in high-performance multi-axis servo control systems are becoming increasingly difficult due to their lack of computing power. Although FPGA chips can meet the computing power requirements of high-performance multi-axis servo control systems, their versatility is insufficient, and the chip is too costly for large-scale use. Therefore, when designing the universal SoC, it is better to directly embed the coprocessor IP core dedicated to accelerating the multi-motor vector control current loop operation into the universal SoC. In this study, a coprocessor IP core that can be flexibly embedded in a universal SoC was designed. The IP core based on time division multiplexing (TDM) technology could accelerate the multi-motor vector control current loop operation according to the hardware–software coordination scheme proposed in this study. The IP was first integrated into a universal SoC to verify its performance, and then the FPGA prototype verification for the SoC was performed under three-axis servo control systems. Secondly, the ASIC implementation of the IP was also conducted based on the CSMC 90 nm process library. The experimental results revealed that the IP had a small area and low power consumption and was suitable for application in universal SoC. Therefore, the cheap and low-power single universal SoC with the coprocessor IP can be suitable for multi-axis servo control