Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems.

In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method's effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method's capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.

With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.

A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.

Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm.

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.

Antimicrobial and anti-biofilm activity of Polygonum chinense L.aqueous extract against Staphylococcus aureus.

Polygonum chinense Linn. (Polygonum chinense L.) is one of the main raw materials of Chinese patent medicines such as Guangdong herbal tea. The increasing antibiotic resistance of S. aureus and the biofilm poses a serious health threat to humans, and there is an urgent need to provide new antimicrobial agents. As a traditional Chinese medicine, the antibacterial effect of Polygonum chinense L. has been reported, but the antibacterial mechanism of Polygonum chinense L.aqueous extract and its effect on biofilm have not been studied in great detail, which hinders its application as an effective antibacterial agent. In this study, the mechanism of action of Polygonum chinense L.aqueous extract on Staphylococcus aureus (S. aureus) and its biofilm was mainly evaluated by morphological observation, flow cytometry and laser confocal experiments. Our findings demonstrate that Polygonum chinense L.aqueous extract has a significant bacteriostatic effect on S. aureus. The result of growth curve exhibits that Polygonum chinense L.aqueous extract presents a significant inhibitory effect against S. aureus. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) reveals that Polygonum chinense L.aqueous extract exerts a potent destruction of the cell wall of S. aureus and a significant inhibitory effect on the formation of S. aureus biofilm. In addition, flow cytometry showed the ability of Polygonum chinense L.aqueous extract to promote apoptosis by disrupting cell membranes of S. aureus. Notably, confocal laser scanning microscopy (CLSM) images illustrated the ability of Polygonum chinense L.aqueous to inhibit the formation of S. aureus biofilms in a dose-dependent manner. These results suggested that Polygonum chinense L.aqueous is a promising alternative antibacterial and anti-biofilm agent for combating infections caused by planktonic and biofilm cells of S. aureus.

Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images.

The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully. In recent decades, the maturation of computer vision technology has provided more possibilities for implementing plant disease detection. Nonetheless, detecting plant diseases is typically hindered by factors such as variations in the illuminance and weather when capturing images and the number of leaves or organs containing diseases in one image. Meanwhile, traditional deep learning-based algorithms attain multiple deficiencies in the area of this research: (1) Training models necessitate a significant investment in hardware and a large amount of data. (2) Due to their slow inference speed, models are tough to acclimate to practical production. (3) Models are unable to generalize well enough. Provided these impediments, this study suggested a Tranvolution detection network with GAN modules for plant disease detection. Foremost, a generative model was added ahead of the backbone, and GAN models were added to the attention extraction module to construct GAN modules. Afterward, the Transformer was modified and incorporated with the CNN, and then we suggested the Tranvolution architecture. Eventually, we validated the performance of different generative models' combinations. Experimental outcomes demonstrated that the proposed method satisfyingly achieved 51.7% (Precision), 48.1% (Recall), and 50.3% (mAP), respectively. Furthermore, the SAGAN model was the best in the attention extraction module, while WGAN performed best in image augmentation. Additionally, we deployed the proposed model on Hbird E203 and devised an intelligent agricultural robot to put the model into practical agricultural use.

Novel CC2D2A compound heterozygous mutations cause Joubert syndrome.

Joubert syndrome (JS) is an autosomal recessive disorder, which is characterized by hypotonia, ataxia, psychomotor delay, and variable occurrences of oculomotor apraxia and neonatal breathing abnormalities. JS is clinically and genetically heterogeneous. The present study investigated a typical JS family. The 'molar tooth sign' was observed in the proband through magnetic resonance imaging. Other symptoms of JS include cerebellar vermis hypoplasia/dysplasia, oculomotor apraxia and intellectual disability. High­throughput sequencing revealed that JS was caused by coiled­coil and C2 domain containing 2A (CC2D2A) compound heterozygous mutations. One CC2D2A allele was affected with a missense mutation, c.2581G>A, which led to a p.Asp861Asn amino acid replacement. The other allele was affected with a c.2848C>T nonsense mutation, which resulted in a truncated CC2D2A protein (p.Arg950Ter). Both of these alterations are novel. Further investigation indicated that the proband's father was the c.2581G>A carrier, whereas the mother was the c.2848C>T carrier. These results indicated that JS in the proband was caused by novel compound heterozygous mutations in CC2D2A, which were inherited from both parents. These findings may be used to establish prenatal molecular diagnostic criteria, which may be beneficial in future pregnancies.