Searching the Adversarial Example in the Decision Boundary

Deep learning technology achieves state of the art result in many computer vision missions. However, some researchers point out that current widely used deep learning architectures are vulnerable to adversarial examples. Adversarial examples are inputs generated by applying small and often imperceptible perturbation to examples in the dataset, such that the perturbed examples can degrade the performance of the deep learning architecture.In the paper, we propose a novel adversarial examples generation method. Adversarial examples generated using this method can have small perturbation and have more diversity compare to adversarial examples generated by other method

Effect of Different Nitrogen Nutrients on DMSP Content in Emiliania huxleyi and Phaeodactylum tricornutum

Dimethylsulfoniopropionate (DMSP) is one of the most important organic sulfur compounds on Earth, and its significance in global sulfur cycling and climate regulation cannot be overlooked, as it plays an indispensable role in these processes. Phytoplankton are the major producers of DMSP in the marine environment, and nitrogen nutrients are key factors influencing the production of DMSP in phytoplankton. This study focused on two algal species, Emiliania huxleyi (a high DMSP producer) and Phaeodactylum tricornutum (a medium DMSP producer), and conducted indoor culture experiments to compare and analyze the content of particulate DMSP (DMSPp) in the algal culture media under different nitrogen nutrient concentrations and types. The study investigated the relationships between overall DMSPp content, algal density, and DMSPp content per individual algal cell. The results indicated that different nitrogen nutrient concentrations and types had a minimal impact on the content of DMSPp per individual cell in E. huxleyi (P > 0.05), suggesting that the DMSPp concentration in the culture media was mostly influenced by algal cell density. Conversely, different nitrogen nutrient concentrations and types had a significant impact on the content of DMSPp per individual cell in P. tricornutum (P < 0.05), indicating that the DMSPp concentration in the culture media was mainly influenced by the content of DMSPp per individual algal cell. For instance, in the case of P. tricornutum, the average DMSPp content per individual cell in the low NO3– concentration (0 μmol/L) culture group was 11 times greater than that in the high NO3– concentration (1 764 μmol/L) culture group. Furthermore, under different nitrogen nutrient types, the average total DMSPp concentration in NaNO3 culture media was three and four times higher than that in the NH4Cl and CH4N2O culture groups, respectively. These differences may be attributed to variations in the physiological effects of DMSP on different algal species

Feasibility of Detecting Sweet Potato (<i>Ipomoea batatas</i>) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

The sweet potato is an essential food and economic crop that is often threatened by the devastating sweet potato virus disease (SPVD), especially in developing countries. Traditional laboratory-based direct detection methods and field scouting are commonly used to rapidly detect SPVD. However, these molecular-based methods are costly and disruptive, while field scouting is subjective, labor-intensive, and time-consuming. In this study, we propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images. We proposed a novel object detector called SPVDet, as well as a lightweight version called SPVDet-Nano, using a single-level feature. These detectors were prototyped based on a small-scale publicly available benchmark dataset (PASCAL VOC 2012) and compared to mainstream feature pyramid object detectors using a leading large-scale publicly available benchmark dataset (MS COCO 2017). The learned model weights from this dataset were then transferred to fine-tune the detectors and directly analyze our self-made SPVD dataset encompassing one category and 1074 objects, incorporating the slicing aided hyper inference (SAHI) technology. The results showed that SPVDet outperformed both its single-level counterparts and several mainstream feature pyramid detectors. Furthermore, the introduction of SAHI techniques significantly improved the detection accuracy of SPVDet by 14% in terms of mean average precision (mAP) in both ground and aerial images, and yielded the best detection accuracy of 78.1% from close-up perspectives. These findings demonstrate the feasibility of detecting SPVD from ground and unmanned aerial vehicle (UAV) high-resolution images using the deep learning-based SPVDet object detector proposed here. They also have great implications for broader applications in high-throughput phenotyping of sweet potatoes under biotic stresses, which could accelerate the screening process for genetic resistance against SPVD in plant breeding and provide timely decision support for production management

YOLOv7-GCA: A Lightweight and High-Performance Model for Pepper Disease Detection

Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, we propose a modified YOLOv7-GCA model based on YOLOv7 for pepper disease detection, which can effectively overcome these challenges. The model introduces three key enhancements: Firstly, lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Secondly, the Cascading fusion network (CFNet) replaces the original feature fusion network, which improves the expression ability of the model in complex backgrounds and realizes multi-scale feature extraction and fusion. Finally, the Convolutional Block Attention Module (CBAM) is introduced to focus on the important features in the images and improve the accuracy and robustness of the model. This study uses the collected dataset, which was processed to construct a dataset of 1259 images with four types of pepper diseases: anthracnose, bacterial diseases, umbilical rot, and viral diseases. We applied data augmentation to the collected dataset, and then experimental verification was carried out on this dataset. The experimental results demonstrate that the YOLOv7-GCA model reduces the parameter count by 34.3% compared to the YOLOv7 original model while improving 13.4% in mAP and 124 frames/s in detection speed. Additionally, the model size was reduced from 74.8 MB to 46.9 MB, which facilitates the deployment of the model on mobile devices. When compared to the other seven mainstream detection models, it was indicated that the YOLOv7-GCA model achieved a balance between speed, model size, and accuracy. This model proves to be a high-performance and lightweight pepper disease detection solution that can provide accurate and timely diagnosis results for farmers and researchers

Warming-driven migration of core microbiota indicates soil property changes at continental scale

Terrestrial species are predicted to migrate northward under global warming conditions, yet little is known about the direction and magnitude of change in microbial distribution patterns. In this continental-scale study with more than 1600 forest soil samples, we verify the existence of core microbiota and lump them into a manageable number of eco-clusters based on microbial habitat preferences. By projecting the abundance differences of eco-clusters between future and current climatic conditions, we observed the potential warming-driven migration of the core microbiota under warming, partially verified by a field warming experiment at Southwest China. Specifically, the species that favor low pH are potentially expanding and moving northward to medium-latitudes (25 degrees-45 degrees N), potentially implying that warm temperate forest would be under threat of soil acidification with warming. The eco-cluster of high-pH with high-annual mean temperature (AMT) experienced significant abundance increases at middle- (35 degrees-45 degrees N) to high-latitudes (> 45 degrees N), especially under Representative Concentration Pathway (RCP) 8.5, likely resulting in northward expansion. Furthermore, the eco-cluster that favors low-soil organic carbon (SOC) was projected to increase under warming scenarios at low-latitudes ( 45 degrees N) the changes in relative abundance of this eco-cluster is inversely related with the temperature variation trends, suggesting microbes-mediated soil organic carbon changes are more responsive to temperature variation in colder areas. These results have vital implications for the migration direction of microbial communities and its potential ecological consequences in future warming scenarios. (C) 2021 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved