Identity-based controlled delegated outsourcing data integrity auditing scheme.

With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red-black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure.

Optimization of Chemical Extraction Conditions of Dietary Fiber from Cistanche deserticola Residues and Its Structural Characteristics and Physicochemical and Functional Properties.

Cistanche deserticola residues are by-products of the industrial production of Cistanche deserticola, which are currently often discarded, resulting in the waste of resources. In order to achieve the efficient utilization of Cistanche deserticola, dietary fiber from Cistanche deserticola residues was extracted chemically and the optimization of the extraction conditions was performed, using the response surface methodology to study the effects of the NaOH concentration, extraction temperature, extraction time, and solid-liquid ratio on the yield of water-soluble dietary fiber (SDF). The structural, physicochemical, and functional properties of the dietary fiber were also investigated. The results showed that the optimal conditions were as follows: NaOH concentration of 3.7%, extraction temperature of 71.7 °C, extraction time of 89.5 min, and solid-liquid ratio of 1:34. The average yield of SDF was 19.56%, which was close to the predicted value of 19.66%. The two dietary fiber types had typical polysaccharide absorption peaks and typical type I cellulose crystal structures, and the surface microstructures of the two dietary fiber types were different, with the surface of SDF being looser and more porous. Both dietary fiber types had good functional properties, with SDF having the strongest water-holding capacity and the strongest adsorption capacity for nitrite, cholesterol, sodium cholate, and glucose, while IDF had a better oil-holding capacity. These results suggest that Cistanche deserticola residues are a good source of dietary fiber and have promising applications in the functional food processing industry.

Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field.

Efficient pest identification and control is critical for ensuring food safety. Therefore, automatic detection of pests has high practical value for Integrated Pest Management (IPM). However, complex field environments and the similarity in appearance among pests can pose a significant challenge to the accurate identification of pests. In this paper, a feature refinement method designed for similar pest detection in the field based on the two-stage detection framework is proposed. Firstly, we designed a context feature enhancement module to enhance the feature expression ability of the network for different pests. Secondly, the adaptive feature fusion network was proposed to avoid the suboptimal problem of feature selection on a single scale. Finally, we designed a novel task separation network with different fusion features constructed for the classification task and the localization task. Our method was evaluated on the proposed dataset of similar pests named SimilarPest5 and achieved a mean average precision (mAP) of 72.7%, which was better than other advanced object detection methods.

Effect of Protease Combined with Heat Treatment on the Volatile Composition and Aroma Quality in Liqueur Wine.

The aim of this paper was to compare the effects of two clarification methods, protease combined with heat treatment and bentonite, on the aroma quality of liqueur wines, and to identify and analyze the overall differences between the basic components and volatile aroma compounds of liqueur wines after the two treatments by chemical analysis, headspace-solid-phase microextraction-gas chromatography/mass spectrometry (HS-SPME-GC/MS), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that total acidity, volatile acidity and pH in liqueur wines after protease combined with heat treatment were not significantly different from those of the blank control, and the ability to remove proteins was equal to that of the bentonite treatment. A total of 58 volatile aroma compounds were detected by HS-SPME-GC/MS. Compared with the blank control group (44 species, total 108.705 mg/L), 52 (83.233 mg/L) and 50 (120.655 mg/L) aroma compounds were detected in the bentonite and protease combined with heat treatments, respectively. Compared with the control and bentonite treatment, the protease combined with heat treatment significantly increased the total content of aromatic compounds in liqueur wines, and the types and contents of olefins, furans and phenols were higher. Among them, the compounds with major contributions in the protease combined with heat treatment were ionone, ß-damascenone, 3-methyl-1-butanol, alpha-terpineol and limonene, which helped increase the content of terpenoids and enhance the floral and fruit aroma of the wine. Meanwhile, linalool, diethyl succinate, 2-methyl-3-heptanone, butanal diethyl acetal, hexanal and n-octanol were six compounds with high content of aromatic compounds unique to liqueur wines after protease combined with heat treatment. The sensory evaluation results were consistent with the results of aromatic compound detection, and the overall quality was better. The results may provide a reference for further exploration of protease-based clarifiers suitable for liqueur wines.

CMRD-Net: a deep learning-based Cnaphalocrocis medinalis damage symptom rotated detection framework for in-field survey.

The damage symptoms of Cnaphalocrocis medinalis (C.medinalis) is an important evaluation index for pest prevention and control. However, due to various shapes, arbitrary-oriented directions and heavy overlaps of C.medinalis damage symptoms under complex field conditions, generic object detection methods based on horizontal bounding box cannot achieve satisfactory results. To address this problem, we develop a Cnaphalocrocis medinalis damage symptom rotated detection framework called CMRD-Net. It mainly consists of a Horizontal-to-Rotated region proposal network (H2R-RPN) and a Rotated-to-Rotated region convolutional neural network (R2R-RCNN). First, the H2R-RPN is utilized to extract rotated region proposals, combined with adaptive positive sample selection that solves the hard definition of positive samples caused by oriented instances. Second, the R2R-RCNN performs feature alignment based on rotated proposals, and exploits oriented-aligned features to detect the damage symptoms. The experimental results on our constructed dataset show that our proposed method outperforms those state-of-the-art rotated object detection algorithms achieving 73.7% average precision (AP). Additionally, the results demonstrate that our method is more suitable than horizontal detection methods for in-field survey of C.medinalis.

Hypolipidemic mechanism of Pleurotus eryngii polysaccharides in high-fat diet-induced obese mice based on metabolomics.

Objective: In this study, the structure of Pleurotus eryngii polysaccharides (PEPs) was characterized, and the mechanism of PEP on obesity and hyperlipidemia induced by high-fat diet was evaluated by metabonomic analysis. Methods: The structure of PEPs were characterized by monosaccharide composition, Fourier transform infrared spectroscopy and thermogravimetry. In animal experiments, H&E staining was used to observe the morphological difference of epididymal adipose tissue of mice in each group. Ultrahigh performance liquid chromatography (UHPLC)-(QE) HFX -mass spectrometry (MS) was used to analyze the difference of metabolites in serum of mice in each group and the related metabolic pathways. Results: The PEPs contained nine monosaccharides: 1.05% fucose, 0.30% arabinose, 17.94% galactose, 53.49% glucose, 1.24% xylose, 23.32% mannose, 1.30% ribose, 0.21%galacturonic acid, and 1.17% glucuronic acid. The PEPs began to degrade at 251°C (T0), while the maximum thermal degradation rate temperature (Tm) appeared at 300°C. The results histopathological observation demonstrated that the PEPs had signifificant hypolipidemic activities. After PEPs intervention, the metabolic profile of mice changed significantly. A total of 29 different metabolites were selected as adjunctive therapy to PEPs, for treatment of obesity and hyperlipidemia-related complications caused by a high-fat diet. These metabolites include amino acids, unsaturated fatty acids, choline, glycerol phospholipids, and other endogenous compounds, which can prevent and treat obesity and hyperlipidemia caused by a high-fat diet by regulating amino acid metabolism, fatty acid metabolism, and changes in metabolic pathways such as that involved in the citric cycle (TCA cycle). Conclusions: The presented results indicate that PEPs treatment can alleviate the obesity and hyperlipidemia caused by a high-fat diet and, thus, may be used as a functional food adjuvant, providing a theoretical basis and technical guidance for the prevention and treatment of high-fat diet-induced obesity and hyperlipidemia.

Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification.

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest. In this paper, according to the pest picture data obtained through the pest detection lamp in the complex natural background and the marking categories of agricultural experts, the pest data set pest rotation detection (PRD21) in different natural environments is constructed. A comparative study of image recognition is carried out through different target detection algorithms. The final experiment proves that the best algorithm for rotation detection improves mean Average Precision by 18.5% compared to the best algorithm for horizontal detection, reaching 78.5%. Regarding Recall, the best rotation detection algorithm runs 94.7%, which is 7.4% higher than horizontal detection. In terms of detection speed, the rotation detection time of a picture is only 0.163s, and the model size is 66.54MB, which can be embedded in mobile devices for fast detection. This experiment proves that rotation detection has a good effect on pests' detection and recognition rate, which can bring new application value and ideas, provide new methods for plant protection, and improve grain yield.

Effects of cinnamon essential oil on the physiological metabolism of Salmonella enteritidis.

Food safety and health are the themes of today's society. As a class of foodborne pathogens, Salmonella enteritidis has become one of the common zoonotic pathogens. Because chemical preservatives have certain harmfulness and have been questioned, it is particularly important to find green and safe natural preservatives. The advantages of plant essential oils (EOs) are that they are green and safe, have a wide range of antibacterials, and are not easy to form drug resistance. In recent years, studies have found that EOs have excellent antibacterial activity, but their antibacterial mechanism has not been conclusive, which has certain limitations in their application in the food field. Cinnamon essential oil (CEO) extracted from dried cinnamon is a secondary metabolite of cells and a very important natural food flavor. More importantly, it is non-toxic to the human body and has been proven to have a good antibacterial effect, but its antibacterial mechanism is still unclear. Therefore, it was of great practical significance to carry out the research on the antibacterial mechanism of CEO on S. enteritidis. In this work, S. enteritidis was used as the test bacteria, and CEO was selected as the antibacterial agent to study the antibacterial mechanisms. By studying the physiological metabolism of S. enteritidis cells by CEO, the influence of CEO on the bacteriostatic mechanism of S. enteritidis was systematically elucidated. The study found that CEO treatment would reduce the activity of bacterial metabolism. It is mainly reflected in the following three aspects: first, the activity of key enzymes in TCA circulation is inhibited, thus affecting the respiration of S. enteritidis. Second, it affects the level of energy metabolism by inhibiting the content of adenosine triphosphate (ATP) and the activity of ATPase. Finally, it can affect the physiological metabolism of bacteria by inhibiting the metabolism of proteins and other substances. Therefore, this article was expected to provide a theoretical basis for the development of new natural food preservatives and the prevention and control of S. enteritidis.

Attention-Based Multiscale Feature Pyramid Network for Corn Pest Detection under Wild Environment.

A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper. Firstly, a large-scale corn pest dataset has been constructed which includes 7741 corn pest images with 10 classes. Secondly, a deep residual network with deformable convolution has been introduced to obtain the features of the corn pest images. To address the detection task of multi-scale corn pests, an attention-based multi-scale feature pyramid network has been developed. Finally, we combined the proposed modules with a two-stage detector into a single network, which achieves the identification and localization of corn pests in an image. Experimental results on the corn pest dataset demonstrate that the proposed method has good performance compared with other methods. Specifically, the proposed method achieves 70.1% mean Average Precision (mAP) and 74.3% Recall at the speed of 17.0 frames per second (FPS), which balances the accuracy and efficiency.

Effects of Cinnamon Essential Oil on Oxidative Damage and Outer Membrane Protein Genes of Salmonella enteritidis Cells.

Salmonella is an important pathogen causing food poisoning. Food safety and health are the themes of today's society. As a class of food-borne pathogens, Salmonella enteritidis had become one of the common zoonotic pathogens. Cinnamon essential oil (CEO) had been reported as an antibacterial agent, but there are few studies on its antibacterial mechanism. This study investigated the effects of CEO on oxidative damage and outer membrane protein genes of Salmonella enteritidis cells. First, the reactive oxygen species content in bacteria treated with different concentrations of cinnamon essential oil was determined by fluorescence spectrophotometry, and the effects of superoxide dismutase (SOD), catalase (CAT) and superoxide dismutase (SOD), and catalase (CAT) and peroxidase (POD) were determined by the kit method. The activity of POD and the content of malondialdehyde (MDA) were investigated to investigate the oxidative damage of CEO to Salmonella enteritidis cells. By analyzing the effect of CEO on the Salmonella enteritidis cell membrane's outer membrane protein gene expression, the mechanism of CEO's action on the Salmonella enteritidis cell membrane was preliminarily discussed. The results showed that CEO treatment had an obvious oxidative damaging effect on Salmonella enteritidis. Compared with the control group, the increase in CEO concentration caused a significant increase in the bacteria ROS content. The observation technique experiment found that with the increase in CEO concentration, the number of stained cells increased, which indicated that CEO treatment would increase the ROS level in the cells, and it would also increase with the increase in CEO concentration, thus causing the oxidation of cells and damage. In addition, CEO treatment also caused the disruption of the balance of the cellular antioxidant enzymes (SOD, CAT, POD) system, resulting in an increase in the content of MDA, a membrane lipid metabolite, and increased protein carbonylation, which ultimately inhibited the growth of Salmonella enteritidis. The measurement results of cell membrane protein gene expression levels showed that the Omp genes to be detected in Salmonella enteritidis were all positive, which indicated that Salmonella enteritidis carried these four genes. Compared with the control group, the relative expressions of OmpF, OmpA and OmpX in the CEO treatment group were significantly increased (p < 0.05), which proved that the cell function was disturbed. Therefore, the toxicity of CEO to Salmonella enteritidis could be attributed to the damage of the cell membrane and the induction of oxidative stress at the same time. It was speculated that the antibacterial mechanism of CEO was the result of multiple effects. This work was expected to provide a theoretical basis for the development of new natural food preservatives and the prevention and control of Salmonella enteritidis.

ASP-Det: Toward Appearance-Similar Light-Trap Agricultural Pest Detection and Recognition.

Automatic pest detection and recognition using computer vision techniques are a hot topic in modern intelligent agriculture but suffer from a serious challenge: difficulty distinguishing the targets of similar pests in 2D images. The appearance-similarity problem could be summarized into two aspects: texture similarity and scale similarity. In this paper, we re-consider the pest similarity problem and state a new task for the specific agricultural pest detection, namely Appearance Similarity Pest Detection (ASPD) task. Specifically, we propose two novel metrics to define the texture-similarity and scale-similarity problems quantitatively, namely Multi-Texton Histogram (MTH) and Object Relative Size (ORS). Following the new definition of ASPD, we build a task-specific dataset named PestNet-AS that is collected and re-annotated from PestNet dataset and also present a corresponding method ASP-Det. In detail, our ASP-Det is designed to solve the texture-similarity by proposing a Pairwise Self-Attention (PSA) mechanism and Non-Local Modules to construct a domain adaptive balanced feature module that could provide high-quality feature descriptors for accurate pest classification. We also present a Skip-Calibrated Convolution (SCC) module that can balance the scale variation among the pest objects and re-calibrate the feature maps into the sizing equivalent of pests. Finally, ASP-Det integrates the PSA-Non Local and SCC modules into a one-stage anchor-free detection framework with a center-ness localization mechanism. Experiments on PestNet-AS show that our ASP-Det could serve as a strong baseline for the ASPD task.

TD-Det: A Tiny Size Dense Aphid Detection Network under In-Field Environment.

It is well recognized that aphid infestation severely reduces crop yield and further leads to significant economic loss. Therefore, accurately and efficiently detecting aphids is of vital importance in pest management. However, most existing detection methods suffer from unsatisfactory performance without fully considering the aphid characteristics, including tiny size, dense distribution, and multi-viewpoint data quality. In addition, existing clustered tiny-sized pest detection methods improve performance at the cost of time and do not meet the real-time requirements. To address the aforementioned issues, we propose a robust aphid detection method with two customized core designs: a Transformer feature pyramid network (T-FPN) and a multi-resolution training method (MTM). To be specific, the T-FPN is employed to improve the feature extraction capability by a feature-wise Transformer module (FTM) and a channel-wise feature recalibration module (CFRM), while the MTM aims at purifying the performance and lifting the efficiency simultaneously with a coarse-to-fine training pattern. To fully demonstrate the validity of our methods, abundant experiments are conducted on a densely clustered tiny pest dataset. Our method can achieve an average recall of 46.1% and an average precision of 74.2%, which outperforms other state-of-the-art methods, including ATSS, Cascade R-CNN, FCOS, FoveaBox, and CRA-Net. The efficiency comparison shows that our method can achieve the fastest training speed and obtain 0.045 s per image testing time, meeting the real-time detection. In general, our TD-Det can accurately and efficiently detect in-field aphids and lays a solid foundation for automated aphid detection and ranking.

Automatic Crop Pest Detection Oriented Multiscale Feature Fusion Approach.

Specialized pest control for agriculture is a high-priority agricultural issue. There are multiple categories of tiny pests, which pose significant challenges to monitoring. Previous work mainly relied on manual monitoring of pests, which was labor-intensive and time-consuming. Recently, deep-learning-based pest detection methods have achieved remarkable improvements and can be used for automatic pest monitoring. However, there are two main obstacles in the task of pest detection. (1) Small pests often go undetected because much information is lost during the network training process. (2) The highly similar physical appearances of some categories of pests make it difficult to distinguish the specific categories for networks. To alleviate the above problems, we proposed the multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network (MFPN) and a novel adaptive feature region proposal network (AFRPN). MFPN can fuse the pest information in multiscale features, which significantly improves detection accuracy. AFRPN solves the problem of anchor and feature misalignment during RPN iterating, especially for small pest objects. In extensive experiments on the multi-category pests dataset 2021 (MPD2021), the proposed method achieved 67.3% mean average precision (mAP) and 89.3% average recall (AR), outperforming other deep learning-based models.

WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method.

Wheat stripe rusts are responsible for the major reduction in production and economic losses in the wheat industry. Thus, accurate detection of wheat stripe rust is critical to improving wheat quality and the agricultural economy. At present, the results of existing wheat stripe rust detection methods based on convolutional neural network (CNN) are not satisfactory due to the arbitrary orientation of wheat stripe rust, with a large aspect ratio. To address these problems, a WSRD-Net method based on CNN for detecting wheat stripe rust is developed in this study. The model is a refined single-stage rotation detector based on the RetinaNet, by adding the feature refinement module (FRM) into the rotation RetinaNet network to solve the problem of feature misalignment of wheat stripe rust with a large aspect ratio. Furthermore, we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021). The performance of WSRD-Net is compared to that of the state-of-the-art oriented object detection models, and results show that WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models. Furthermore, through the comparison with horizontal object detection models, it is found that WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas.

Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection.

An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection.

Study on the application value of BACs-on-Beads technology combined with chromosome karyotype analysis in prenatal diagnosis.

Background: Bacterial artificial chromosome (BAC) marker-microsphere identification/separation technique [BACs-on-Beads (BoBs)] not only has a high detection rate for major chromosomal changes, but also for the other 9 microdeletion syndromes. In this study, the application value of BoBs combined with karyotype detection in prenatal diagnosis was evaluated. Methods: The amniotic fluid samples of 132 pregnant women with prenatal diagnosis indications in Harbin Red Cross Central Hospital from June 2018 to June 2019 were collected and subjected to the detection of BoBs and routine karyotyping. Results: Among the 132 pregnant women's amniotic fluid samples, 30 cases were abnormal in BoBs detection, with a detection rate of 22.73%, and 29 cases were abnormal in chromosome karyotype analysis, with a detection rate of 21.97%. Among them, 1 case of DiGeorge Type I microdeletion syndrome BoBs was successfully detected. The karyotype analysis failed to detect the same syndrome; the total coincidence rate of two methods was 99.24%, the positive coincidence rate was 100.00%, and the negative coincidence rate was 99.03%; the sensitivity, specificity and positive predictive value (PPV), and negative predictive value (NPV) of the chromosome karyotype analysis was 96.67%, 100%, and 99.03%, respectively; the accuracy, specificity, and PPV/NPV of BoBs detection were 100%. Conclusions: When BoBs technology is combined with chromosome karyotype analysis, it can increase the detection rate of fetal chromosomal abnormalities, which could provide a basis for clinical prevention and follow-up diagnosis and treatment.

Prior knowledge auxiliary for few-shot pest detection in the wild.

One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios. Firstly, a prior-knowledge auxiliary architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection data set has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean average precision (mAP) and mean average recall (mAR). The results show the promising effectiveness of the proposed few-shot detection architecture.

An automatic system for pest recognition and forecasting.

BACKGROUND: Pests cause significant damage to agricultural crops and reduce crop yields. Use of manual methods of pest forecasting for integrated pest management is labor-intensive and time-consuming. Here, we present an automatic system for monitoring pests in large fields, with the aim of replacing manual forecasting. The system comprises an automatic detection and counting system and a human-computer data statistical fitting system. Image data sets of the target pests from large fields are first input into the system. The number of pests in the image is then counted both manually and using the automatic system. Finally, a mapping relationship between counts obtained using the automated system and by agricultural experts is established using the statistical fitting system. RESULTS: Trends in the pest-count curves produced using the manual and automated counting methods were very similar. To sample the number of pests for manual statistics, plants were shaken to transfer the pests from the plant to a plate. Hence, pests hiding within plant crevices were also sampled and included in the count, whereas the automatic method counted only the pests visible in the images. Therefore, the computer index threshold was much lower than the manual index threshold. However, the proposed system correctly reflected trends in pest numbers obtained using computer vision. CONCLUSION: The experimental results demonstrate that our automatic pest-monitoring system can generate pest grades and can replace manual forecasting methods in large fields. © 2021 Society of Chemical Industry.

A new ab initio potential energy surface for the NeCO complex with the vibrational coordinate dependence.

A new high quality three-dimensional potential energy surface for the Ne-CO van der Waals complex is developed using the CCSD(T) method and avqz∕avqz+33221 basis set. The ab initio calculation is performed in a total of 1365 configurations with supermolecule method. There is a single global minimum located in a nearly T-shaped geometry. The global minimum energy is -49.4090 cm(-1) at R(e)=6.40a(0) and θ(e)=82.5(∘) for V(00). Using the three-dimensional potential energy surface, we have calculated bound rovibrational energy levels up to J = 10 including the Coriolis coupling terms. Compared with the experimental transition frequencies, the theoretical results are in good agreement with the experimental results.