The effect of Aceria litchii (Keifer) infestation on the surface properties of litchi leaf hosts

BACKGROUND

The wettability of target crop surfaces affects pesticide wetting and deposition. The structure and properties of the leaf surface of litchi leaves undergo severe changes after infestation by Aceria litchii (Keifer). The objective of this study was to systematically investigate the surface texture and wettability of litchi leaves infested.

RESULTS

Firstly, the investigation focused on the surface structure and physicochemical properties of litchi leaves infested with Aceria litchii. Subsequently, different levels of Contact Angle (CA) were measured individually on the infested litchi leaves. Lastly, Surface Free Energy (SFE) and its polar and dispersive components were calculated using the Owens-Wendt-Rabel-Kaelble (OWRK) method. The outcomes revealed distinctive 3D surface structures of the erineum at various stages of mycorrhizal growth. At stage NO. 1, the height of the fungus displayed a peaked appearance, with the skewness value indicating a surface characterized by more crests. In contrast, at stages NO. 2 and NO. 3, the surface appeared relatively flat. Furthermore, post-infestation of litchi leaves, the CA of droplets on the abaxial surface of diseased leaves exhibited an increase, while the SFE value on the abaxial surface of leaves decreased significantly, in contrast to the abaxial surface of healthy leaves.

CONCLUSION

The infestation behavior of Aceria litchii changed the surface structure and chemistry of litchi leaves, which directly affected the CA value of foliar liquids and the SFE value of leaves, changing the surface wettability of litchi leaves from hydrophobic to superhydrophobic. This study provides useful information for improving the wetting and deposition behavior of liquid droplets on the surface of infested leaves. © 2024 Society of Chemical Industry.

Assessing the efficiency of UAV for pesticide application in disease management of peanut crop

BACKGROUND

Effective utilization of plant protection UAVs in peanut cultivation management necessitates a comprehensive grasp of how application volume rates and pesticides influence peanut leaf spot and rust control. This study aimed to compare the effects of application volume rates and pesticides on droplet deposition, disease, leaf retention rate and peanut yield. A T20 plant protection unmanned aerial vehicle (UAV) sprayer was used to apply four various pesticide doses. In comparison, a knapsack sprayer was used to spray with an application volume rate of 450 L ha-1.

RESULTS

The results showed a significant difference in droplet deposition between the plant protection UAVs and the electric knapsack sprayer. In the pesticide treatment with an application volume rate of 15.0 L ha-1, there was no significant difference in the deposition on the peanut canopy of each pesticide treatment, but there was a significant difference in the deposition on the ground in the treatment with adding vegetable oil adjuvant. The treatment with added vegetable oil additives showed the worst performance. The treatment with an application volume rate of 22.5 L ha-1 showed the best performance, with the leaf spot control effect being only 0.3% lower than that of the electric knapsack sprayer.

CONCLUSION

Plant protection UAV spraying is feasible to control peanut diseases. Considering the operational effectiveness of the plant protection UAV and application volume rate, it is recommended to use an application volume rate of 22.5 L ha-1 without adding vegetable oil adjuvants for field operations. © 2024 Society of Chemical Industry.

Triboelectric micro-flexure-sensitive fiber electronics

Developing fiber electronics presents a practical approach for establishing multi-node distributed networks within the human body, particularly concerning triboelectric fibers. However, realizing fiber electronics for monitoring micro-physiological activities remains challenging due to the intrinsic variability and subtle amplitude of physiological signals, which differ among individuals and scenarios. Here, we propose a technical approach based on a dynamic stability model of sheath-core fibers, integrating a micro-flexure-sensitive fiber enabled by nanofiber buckling and an ion conduction mechanism. This scheme enhances the accuracy of the signal transmission process, resulting in improved sensitivity (detectable signal at ultra-low curvature of 0.1 mm-1; flexure factor >21.8% within a bending range of 10°.) and robustness of fiber under micro flexure. In addition, we also developed a scalable manufacturing process and ensured compatibility with modern weaving techniques. By combining precise micro-curvature detection, micro-flexure-sensitive fibers unlock their full potential for various subtle physiological diagnoses, particularly in monitoring fiber upper limb muscle strength for rehabilitation and training.

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

BACKGROUND

Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness.

RESULTS

The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices.

CONCLUSION

The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards

BACKGROUND

Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling.

RESULTS

Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling.

CONCLUSION

UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry.

Causal effects of diabetic retinopathy on depression, anxiety and bipolar disorder in the European population: a Mendelian randomization study

PURPOSE

To verify the causal effects of diabetic retinopathy (DR) on depression, anxiety and bipolar disorder (BD).

METHODS

Mendelian randomization (MR) analysis was performed to identify the causal relationships between DR and depression or anxiety or BD via using DR-related GWAS data (14,584 cases and 176,010 controls), depression-related GWAS data (59,851 cases and 113,154 controls), anxiety-related GWAS data (7016 cases and 14,745 controls) and BD-related GWAS data (41,917 cases and 371,549 controls). The inverse-variance weighted (IVW) model was adopted to estimate the causal relationship. The outcome was expressed as odds ratio (OR) with 95% confidence intervals (CI).

RESULTS

The MR analysis results presented that DR was causally associated with a significantly increased risk of BD in the European population (IVW, OR = 1.06, 95%CI [1.03, 1.08], P = 2.44 × 10-6), while DR was unable to causally influence the risk of depression (IVW, OR = 1.01, 95%CI [0.99, 1.04], P = 0.32) and anxiety (IVW, OR = 0.97, 95%CI [0.89, 1.06], P = 0.48) in the European population. Subgroup analysis based on BD identified DR causally increased the risk of bipolar I disorder (BD I) but not bipolar II disorder (BD II). Sensitivity analysis results did not show any pleiotropy and heterogeneity in both groups of analyses, indicating that the results were stable and reliable.

CONCLUSIONS

The results of the current MR analysis indicated a causal relationship between DR and BD in the European population, while there was no causal connection between DR and depression or anxiety. However, further research is needed to confirm these conclusions.

Explanatory Object Part Aggregation for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on labeled images from seen classes. Most existing ZSL methods focus on optimizing feature spaces or generating visual features of unseen classes, both in conventional ZSL and generalized zero-shot learning (GZSL). However, since the learned feature spaces are suboptimal, there exists many virtual connections where visual features and semantic attributes are not corresponding to each other. To reduce virtual connections, in this paper, we propose to discover comprehensive and fine-grained object parts by building explanatory graphs based on convolutional feature maps, then aggregate object parts to train a part-net to obtain prediction results. Since the aggregated object parts contain comprehensive visual features for activating semantic attributes, the virtual connections can be reduced by a large extent. Since part-net aims to extract local fine-grained visual features, some attributes related to global structures are ignored. To take advantage of both local and global visual features, we design a feature distiller to distill local features into a master-net which aims to extract global features. The experimental results on AWA2, CUB, FLO, and SUN dataset demonstrate that our proposed method obviously outperforms the state-of-the-arts in both conventional ZSL and GZSL tasks.

Visible and NIR microscopic hyperspectrum reconstruction from RGB images with deep convolutional neural networks

We investigate the microscopic hyperspectral reconstruction from RGB images with a deep convolutional neural network (DCNN) in this paper. Based on the microscopic hyperspectral imaging system, a homemade dataset consisted of microscopic hyperspectral and RGB image pairs is constructed. For considering the importance of spectral correlation between neighbor spectral bands in microscopic hyperspectrum reconstruction, the 2D convolution is replaced by 3D convolution in the DCNN framework, and a metric (weight factor) used to evaluate the performance reconstructed hyperspectrum is also introduced into the loss function used in training. The effects of the dimension of convolution kernel and the weight factor in the loss function on the performance of the reconstruction model are studied. The overall results indicate that our model can show better performance than the traditional models applied to reconstruct the hyperspectral images based on DCNN for the public and the homemade microscopic datasets. In addition, we furthermore explore the microscopic hyperspectrum reconstruction from RGB images in infrared region, and the results show that the model proposed in this paper has great potential to expand the reconstructed hyperspectrum wavelength range from the visible to near infrared bands.

Zinc-oxide nanoparticles ameliorated the phytotoxic hazards of cadmium toxicity in maize plants by regulating primary metabolites and antioxidants activity

Cadmium stress is a major threat to plant growth and survival worldwide. The current study aims to green synthesis, characterization, and application of zinc-oxide nanoparticles to alleviate cadmium stress in maize (Zea mays L.) plants. In this experiment, two cadmium levels (0, 0.6 mM) were applied to check the impact on plant growth attributes, chlorophyll contents, and concentration of various primary metabolites and antioxidants under exogenous treatment of zinc-oxide nanoparticles (25 and 50 mg L-1) in maize seedlings. Tissue sampling was made 21 days after the zinc-oxide nanoparticles application. Our results showed that applying cadmium significantly reduced total chlorophyll and carotenoid contents by 52.87% and 23.31% compared to non-stress. In comparison, it was increased by 53.23%, 68.49% and 9.73%, 37.53% with zinc-oxide nanoparticles 25, 50 mg L-1 application compared with cadmium stress conditions, respectively. At the same time, proline, superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase contents were enhanced in plants treated with cadmium compared to non-treated plants with no foliar application, while it was increased by 12.99 and 23.09%, 23.52 and 35.12%, 27.53 and 36.43%, 14.19 and 24.46%, 14.64 and 37.68% by applying 25 and 50 mg L-1 of zinc-oxide nanoparticles dosages, respectively. In addition, cadmium toxicity also enhanced stress indicators such as malondialdehyde, hydrogen peroxide, and non-enzymatic antioxidants in plant leaves. Overall, the exogenous application of zinc-oxide nanoparticles (25 and 50 mg L-1) significantly alleviated cadmium toxicity in maize. It provides the first evidence that zinc-oxide nanoparticles 25 ~ 50 mg L-1 can be a candidate agricultural strategy for mitigating cadmium stress in cadmium-polluted soils for safe agriculture practice.

Tryptophan Seed Treatment Improves Morphological, Biochemical, and Photosynthetic Attributes of the Sunflower under Cadmium Stress

Tryptophan, as a signal molecule, mediates many biotic and environmental stress-induced physiological responses in plants. Therefore, an experiment was conducted to evaluate the effect of tryptophan seed treatment in response to cadmium stress (0, 0.15, and 0.25 mM) in sunflower plants. Different growth and biochemical parameters were determined to compare the efficiency of the treatment agent. The results showed that cadmium stress reduced the growth attributes, including root and shoot length, dry and fresh weight, rate of seed germination, and the number of leaves. Cadmium stress also significantly reduced the contents of chlorophyll a, b, and total chlorophyll, carotenoid contents, phenolics, flavonoids, anthocyanin, and ascorbic acid. Whereas cadmium stress (0.15 and 0.25 mM) enhanced the concentrations of malondialdehyde (45.24% and 53.06%), hydrogen peroxide (-11.07% and 5.86%), and soluble sugars (28.05% and 50.34%) compared to the control. Tryptophan treatment decreased the effect of Cd stress by minimizing lipid peroxidation. Seed treatment with tryptophan under cadmium stress improved the root (19.40%) and shoot length (38.14%), root (41.90%) and shoot fresh weight (13.58%), seed germination ability (13.79%), average leaf area (24.07%), chlorophyll b (51.35%), total chlorophyll (20.04%), carotenoids (43.37%), total phenolic (1.47%), flavonoids (19.02%), anthocyanin (26.57%), ascorbic acid (4%), and total soluble proteins (12.32%) compared with control conditions. Overall, the tryptophan seed treatment showed positive effects on sunflower plants' growth and stress tolerance, highlighting its potential as a sustainable approach to improve crop performance.

[Effectiveness analysis of hybrid endoscopic submucosal dissection in patients with colorectal epithelium-derived tumors]

Objective: To compare the clinical benefits of classic endoscopic submucosal dissection (ESD) and hybrid ESD for the treatment of colorectal epithelium-derived tumors. Methods: The current investigation was a retrospective multicenter study of 418 patients who underwent ESD between January 2015 and April 2021 at Beijing Jishuitan Hospital. The patients were assigned to one of two groups based on the surgical procedure they underwent; a classic ESD group or a hybrid ESD group. The primary outcome was the rate of en bloc resection and complete resection. SPSS 26.0 was used for statistical analysis. Homogeneity of variance was assessed via Cochran's test. Normally distributed data with homogeneity of variance were analyzed via the t-test for independent samples. Non-normally distributed data and data with unequal variance were analyzed via the Kruskal-Wallis non-parametric test. Categorical data were analyzed via the Chi-square test or Fisher's exact test. Multivariable assessment was performed via logistic regression analysis. Results: The en bloc resection rates [89.4% (84/94) vs. 87.0% (194/223), χ2=0.34, P=0.558] and complete resection rates [85.1% (80/94) vs. 82.1% (183/223), χ2=0.33, P=0.510] were similar. Compared with classic ESD, procedures were shorter in the hybrid ESD group [22(7, 213) vs. 47(12, 680) min, Z=0.23, P<0.001], dissection was completed more rapidly [0.14(0.02, 0.32) vs. 0.10(0.02, 0.41) cm2/min, Z=0.08, P<0.001], and there was a higher rate of perforation (9.6% vs. 2.2%, χ2=2.67, P=0.006). Laterally spreading tumor granular type nodular mixed, non-granular type pseudo-depressed, flat-elevated type (odds ratio 2.826, P=0.012), and tumor location (odds ratio 6.970, P=0.005) were independently associated with complete resection in the hybrid ESD group. Conclusion: Classic ESD and hybrid ESD had similar en bloc and complete resection rates for colorectal epithelium-derived tumors, but hybrid ESD had shorter operation times. With respect to hybrid ESD, factors associated with failure of complete resection included lesion type and crossing tissue boundaries.

An efficient approach to detect and track winter flush growth of litchi tree based on UAV remote sensing and semantic segmentation

The immature winter flush affects the flower bud differentiation, flowering and fruit of litchi, and then seriously reduces the yield of litchi. However, at present, the area estimation and growth process monitoring of winter flush still rely on manual judgment and operation, so it is impossible to accurately and effectively control flush. An efficient approach is proposed in this paper to detect the litchi flush from the unmanned aerial vehicle (UAV) remoting images of litchi crown and track winter flush growth of litchi tree. The proposed model is constructed based on U-Net network, of which the encoder is replaced by MobeilNetV3 backbone network to reduce model parameters and computation. Moreover, Convolutional Block Attention Module (CBAM) is integrated and convolutional layer is added to enhance feature extraction ability, and transfer learning is adopted to solve the problem of small data volume. As a result, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) on the flush dataset are increased from 90.95% and 83.3% to 93.4% and 85%, respectively. Moreover, the size of the proposed model is reduced by 15% from the original model. In addition, the segmentation model is applied to the tracking of winter flushes on the canopy of litchi trees and investigating the two growth processes of litchi flushes (late-autumn shoots growing into flushes and flushes growing into mature leaves). It is revealed that the growth processes of flushes in a particular branch region can be quantitatively analysed based on the UAV images and the proposed semantic segmentation model. The results also demonstrate that a sudden drop in temperature can promote the rapid transformation of late-autumn shoots into flushes. The method proposed in this paper provide a new technique for accurate management of litchi flush and a possibility for the area estimation and growth process monitoring of winter flush, which can assist in the control operation and yield prediction of litchi orchards.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion. This drawback obstructed the accurate extraction of phenotypic parameters. Hence, this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques. The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network. The findings demonstrated that our network is stable and robust, as it can effectively complete diverse leaf point cloud morphologies, missing ratios, and multi-missing scenarios. A novel framework is presented for 3D plant reconstruction using a single-view RGB-D (Red, Green, Blue and Depth) image. This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions. Additionally, the extracted leaf area parameters, based on triangular mesh, were compared with the measured values. The outcomes revealed that prior to the point cloud completion, the R2 value of the flowering Chinese Cabbage's estimated leaf area (in comparison to the standard reference value) was 0.9162. The root mean square error (RMSE) was 15.88 cm2, and the average relative error was 22.11%. However, post-completion, the estimated value of leaf area witnessed a significant improvement, with an R2 of 0.9637, an RMSE of 6.79 cm2, and average relative error of 8.82%. The accuracy of estimating the phenotypic parameters has been enhanced significantly, enabling efficient retrieval of such parameters. This development offers a fresh perspective for non-destructive identification of plant phenotypes.

Global Analysis of Lysine Lactylation of Germinated Seeds in Wheat

Protein lactylation is a newly discovered posttranslational modification (PTM) and is involved in multiple biological processes, both in mammalian cells and rice grains. However, the function of lysine lactylation remains unexplored in wheat. In this study, we performed the first comparative proteomes and lysine lactylomes during seed germination of wheat. In total, 8000 proteins and 927 lactylated sites in 394 proteins were identified at 0 and 12 h after imbibition (HAI). Functional enrichment analysis showed that glycolysis- and TCA-cycle-related proteins were significantly enriched, and more differentially lactylated proteins were enriched in up-regulated lactylated proteins at 12 HAI vs. 0 HAI through the KEGG pathway and protein domain enrichment analysis compared to down-regulated lactylated proteins. Meanwhile, ten particularly preferred amino acids near lactylation sites were found in the embryos of germinated seeds: AA*KlaT, A***KlaD********A, KlaA**T****K, K******A*Kla, K*Kla********K, KlaA******A, Kla*A, KD****Kla, K********Kla and KlaG. These results supplied a comprehensive profile of lysine lactylation of wheat and indicated that protein lysine lactylation played important functions in several biological processes.

Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging

Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with R_p² of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with R_p² of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with R_p² of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.

A 2-Stage Root Analog Implant with Compact Structure, Uniform Roughness, and High Accuracy

Immediate implant placement has the advantages of shortening the operation time, reducing the treatment cycle and cost. At present, this technology has been used widely, but the indications of immediate implantation are still limited. Here, a novel type of root analog implant (RAI) was manufactured by selective laser melting technology to address the limitation. Under optimized condition, RAIs were printed with the internal density of 99.73% and the uniform surface roughness of 11 μm (Sa). Besides, the deviation between RAI specimen and design models is controlled within 0.15 mm after optimizing scanning parameters. The substrate printed could promote human bone marrow stromal cell proliferation, spreading, and osteogenic differentiation. The bone-implant contact (BIC, 75% ± 7%) and bone volume/total volume (BV/TV, 74% ± 7%) of RAIs were significantly higher than that of conventional implants (BIC, 66% ± 5%; BV/TV, 62% ± 5%) in in vivo experiments. Further, customized abutments were designed for the RAIs, improving the masticatory ability of the beagle dogs after crown restoration. This study aims to design a personalized 2-stage RAI with compact structure and uniform roughness, in order to achieve better fracture resistance, initial osseointegration efficiency, and dispersed stress in immediate implantation. It provides a certain guiding value for standardizing the manufacture and clinical application of RAI in immediate implantation.

Detection of Rice Pests Based on Self-Attention Mechanism and Multi-Scale Feature Fusion

In recent years, the occurrence of rice pests has been increasing, which has greatly affected the yield of rice in many parts of the world. The prevention and cure of rice pests is urgent. Aiming at the problems of the small appearance difference and large size change of various pests, a deep neural network named YOLO-GBS is proposed in this paper for detecting and classifying pests from digital images. Based on YOLOv5s, one more detection head is added to expand the detection scale range, the global context (GC) attention mechanism is integrated to find targets in complex backgrounds, PANet is replaced by BiFPN network to improve the feature fusion effect, and Swin Transformer is introduced to take full advantage of the self-attention mechanism of global contextual information. Results from experiments on our insect dataset containing Crambidae, Noctuidae, Ephydridae, and Delphacidae showed that the average mAP of the proposed model is up to 79.8%, which is 5.4% higher than that of YOLOv5s, and the detection effect of various complex scenes is significantly improved. In addition, the paper analyzes and discusses the generalization ability of YOLO-GBS model on a larger-scale pest data set. This research provides a more accurate and efficient intelligent detection method for rice pests and others crop pests.

Evaluation of aerial spraying application of multi-rotor unmanned aerial vehicle for Areca catechu protection

Multi-rotor unmanned aerial vehicle (UAV) is a new chemical application tool for tall stalk tropical crop Areca catechu, which could improve deposit performance, reduce operator healthy risk, and increase spraying efficiency. In this work, a spraying experiment was carried out in two A. catechu fields with two leaf area index (LAI) values, and different operational parameters were set. Spray deposit quality, spray drift, and ground loss were studied and evaluated. The results showed that the larger the LAI of A. catechu, the lesser the coverage of the chemical deposition. The maximum coverage could reach 4.28% and the minimum 0.33%. At a flight speed of 1.5 m/s, sprayed droplets had the best penetration and worst ground loss. The overall deposition effect was poor when the flight altitudes were greater than 11.09 m and the flight speed was over 2.5 m/s. Comparing flight speed of 2.5 to 1.5 m/s, the overall distance of 90% of the total drift increased to double under the same operating parameters. This study presents reference data for UAV chemical application in A. catechu protection.

Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm

INTRODUCTION

Current aerial plant protection with Unmanned Aerial Vehicles (UAV) usually applies full coverage route planning, which is challenging for plant protection operations in the orchards in South China. Because the fruit planting has the characteristics of dispersal and irregularity, full-coverage route spraying causes re-application as well as missed application, resulting in environmental pollution. Therefore, it is of great significance to plan an efficient, low-consumption and accurate plant protection route considering the flight characteristics of UAVs and orchard planting characteristics.

METHODS

This study proposes a plant protection route planning algorithm to solve the waypoint planning problem of UAV multi-objective tasks in orchard scenes. By improving the heuristic function in Ant Colony Optimization (ACO), the algorithm combines corner cost and distance cost for multi-objective node optimization. At the same time, a sorting optimization mechanism was introduced to speed up the iteration speed of the algorithm and avoid the influence of inferior paths on the optimal results. Finally, Multi-source Ant Colony Optimization (MS-ACO) was proposed after cleaning the nodes of the solution path.

RESULTS

The simulation results of the three test fields show that compared with ACO, the path length optimization rate of MS-ACO are 3.89%, 4.6% and 2.86%, respectively, the optimization rate of total path angles are 21.94%, 45.06% and 55.94%, respectively, and the optimization rate of node numbers are 61.05%, 74.84% and 75.47%, respectively. MS-ACO can effectively reduce the corner cost and the number of nodes. The results of field experiments show that for each test field, MS-ACO has a significant optimization effect compared with ACO, with an optimization rate of energy consumption per meter of more than 30%, the optimization rate of flight time are 46.67%, 56% and 59.01%, respectively, and the optimization rate of corner angle are 50.76%, 61.78% and 71.1%, respectively.

DISCUSSION

The feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can optimize the spraying path according to the position of each fruit tree and the flight characteristics of UAV, effectively reduce the energy consumption of UAV flight, improve the operating efficiency, and provide technical reference for the waypoint planning of plant protection UAV in the orchard scene.

Numerical simulation and verification of rotor downwash flow field of plant protection UAV at different rotor speeds

In aerial spraying of plant protection UAVs, the continuous reduction of pesticides is an objective process. Under the condition of constant flight state (speed and altitude), the change of pesticide loading will inevitably lead to the shift of lift force and rotor speed generated by UAV rotor rotation, which will change the distribution of the rotor flow field and affect the effect of aerial spraying operation of plant protection UAV. Therefore, the rotor speed of UAV is taken as the research object in this paper, and the adaptive refinement physical model based on the Lattice Boltzmann Method (LBM) is used to numerically simulate the rotor flow field of the quadrotor plant-protection UAV at different speeds. A high-speed particle image velocimeter (PIV) was used to obtain and verify the motion state of the droplets emitted from the fan nozzle in the rotor flow field at different speeds. The results show that, with the increase of rotor speed, the maximum velocity and vorticity of the wind field under the rotor increase gradually, the top wind speed can reach 13m/s, and the maximum vorticity can reach 589.64s ^-1. Moreover, the maximum velocity flow value is mainly concentrated within 1m below the rotor, and the maximum vorticity value is primarily concentrated within 0.5m. However, with the increase of time, the ultimate value of velocity and vorticity decreases due to the appearance of turbulence, and the distribution of velocity and vorticity are symmetrically distributed along the centre line of the fuselage, within the range of (-1m, 1m) in the X direction. It is consistent with the motion state of droplets under the action of the rotor downwash flow field obtained by PIV. The study results are expected to reveal and understand the change law of the rotor flow field of plant protection UAVs with the dynamic change of pesticide loading to provide a theoretical basis for the development of precise spraying operation mode of plant protection UAVs and improve the operation effect.

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

BACKGROUND

The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.

RESULTS

In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.

CONCLUSIONS

Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Citrus green fruit detection via improved feature network extraction

INTRODUCTION

It is crucial to accurately determine the green fruit stage of citrus and formulate detailed fruit conservation and flower thinning plans to increase the yield of citrus. However, the color of citrus green fruits is similar to the background, which results in poor segmentation accuracy. At present, when deep learning and other technologies are applied in agriculture for crop yield estimation and picking tasks, the accuracy of recognition reaches 88%, and the area enclosed by the PR curve and the coordinate axis reaches 0.95, which basically meets the application requirements.To solve these problems, this study proposes a citrus green fruit detection method that is based on improved Mask-RCNN (Mask-Region Convolutional Neural Network) feature network extraction.

METHODS

First, the backbone networks are able to integrate low, medium and high level features and then perform end-to-end classification. They have excellent feature extraction capability for image classification tasks. Deep and shallow feature fusion is used to fuse the ResNet(Residual network) in the Mask-RCNN network. This strategy involves assembling multiple identical backbones using composite connections between adjacent backbones to form a more powerful backbone. This is helpful for increasing the amount of feature information that is extracted at each stage in the backbone network. Second, in neural networks, the feature map contains the feature information of the image, and the number of channels is positively related to the number of feature maps. The more channels, the more convolutional layers are needed, and the more computation is required, so a combined connection block is introduced to reduce the number of channels and improve the model accuracy. To test the method, a visual image dataset of citrus green fruits is collected and established through multisource channels such as handheld camera shooting and cloud platform acquisition. The performance of the improved citrus green fruit detection technology is compared with those of other detection methods on our dataset.

RESULTS

The results show that compared with Mask-RCNN model, the average detection accuracy of the improved Mask-RCNN model is 95.36%, increased by 1.42%, and the area surrounded by precision-recall curve and coordinate axis is 0.9673, increased by 0.3%.

DISCUSSION

This research is meaningful for reducing the effect of the image background on the detection accuracy and can provide a constructive reference for the intelligent production of citrus.

[Mechanism of nerve growth factor promotes angiogenesis and skeletal muscle fiber remodeling in a mouse hindlimb ischemic model]

Objective: To explore the mechanism of nerve growth factor (NGF) in the skeletal muscle fiber remodeling in ischemic limbs during therapeutic angiogenesis. Methods: Eighteen female mice with SPF grade, 6 weeks old and 25-30 g weighed were randomly allocated to sham-operated group (n=6), blank control group (n=6) and NGF gene transfection group (n=6). The left hindlimb ischemia models were established by ligating the femoral artery in blank control group and NGF gene transfection group. Seven days after the operation, mice in the three groups were separately injected with normal saline, empty plasmids, and NGF plasmids. Gastrocnemius of left hindlimbs was harvested after the blood perfusion assessment of the ischemic limb on the 21st postoperative day. The gastrocnemius muscle specimens were stained with HE, CD31 and proliferating cell nuclear antigen (PCNA) immunohistochemistry staining, the mRNA expressions of myosin heavy chain-Ⅰ(MHC-Ⅰ), MHC-Ⅱa and MHC-Ⅱb were measured by real-time PCR, and the protein level of NGF and peroxisome proliferator-activated receptors-β/δ (PPAR β/δ) were detected by Western blot. The expression of cytochrome C oxidase (COX), isocitrate dehydrogenase (IDH) and adenosine triphosphate (ATP) were examined by enzyme-linked immunosorbent assay (ELISA). Results: On the 21st day after operation, the blood perfusion of the ischemic limb in NGF gene transfection group was (195.70±9.99)PU, which was lower than that in sham-operated group (312.15±17.32)PU (P=0.001), while it was higher than that in blank control group (82.11±8.55)PU (P=0.001). The degree of muscle atrophy in the NGF gene transfection group was lower than that in the blank control group. The capillary density of NGF gene transfection group (0.34±0.05) was higher than that of sham-operated group (0.11±0.03) and blank control group (0.27±0.04) (P<0.05). The endothelial cell proliferation index in NGF gene transfection group (0.39±0.19) was significantly higher than that in sham-operated group (0.18±0.01) and blank control group (0.25±0.14) (P<0.05). The expression of NGF, PPAR β/δ, COX, IDH, ATP, and MHC-Ⅰ mRNA in NGF gene transfection group were significantly higher than those in sham-operated group and blank control group (P<0.05). Conclusions: NGF gene transfection can promote angiogenesis in the ischemic limbs of mice, increase the blood perfusion, and thus induce the remodeling of skeletal muscle fibers to type Ⅰ. This process may be related to NGF-induced PPAR β/δ expression and promote the cellular aerobic metabolism in skeletal muscle.

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R ² = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R ² = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

Control Efficacy and Deposition Characteristics of an Unmanned Aerial Spray System Low-Volume Application on Corn Fall Armyworm Spodoptera frugiperda

As a major global pest, fall armyworm (FAW), Spodoptera frugiperda, invaded China in 2019, which has seriously threatened the safety of China's food production and raised widespread concerns. As a new low-volume application technology, an unmanned aerial spray system (UASS) is playing an important role in the control of FAW in China. However, the studies on the effect of the water application volume on the efficacy of FAW using UASS have been limited. In this study, Kromekote® cards were used to sample the deposition. The method of using a sampling pole and sampling leaf for the determination of deposition. Four water application volumes (7.5, 15.0, 22.5, and 30.0 L/ha) were evaluated with regard to the corn FAW control efficacy. A blank control was used as a comparison. The control efficacy was assessed at 1, 3, 7, and 14 days after treatment (DAT). The tested results showed that sampling methods have a significant effect on deposition results. The number of spray deposits and coverage on the sampling pole were 35 and 40% higher than those on the sampling leaves, respectively. The deposition and control efficacy gradually increased as the water application volume increased. The control efficacy at 14 DAT under different water application volumes was in the range of 59.4-85.4%. These data suggest that UASS spraying can be used to achieve a satisfying control of FAW, but the control efficacy of the water application volume of 30.0 and 22.5 L/ha did not differ significantly. Considering work efficiency, a water application volume of 22.5 L/ha is recommended for field operation.

The identification and first report of Alternaria alternata causing leaf spot on Gaillardia pulchella Foug. in Shandong province of China

Gaillardia pulchella Foug., belonging to the family Asteraceae, is an annual herb commonly seen in tropical America and China. It is often used as ornamental flowers because of its bright color, long flowering period and simple cultivation and management. In June 2021, leaf spot on G. pulchella with ∼40% disease incidence was observed in Laoshan scenic spot of Qingdao, Shandong Province, China. Initial symptoms on leaves appeared as light yellow to brown round or oval spots with dark brown borders, and the lesion area gradually expanded and the color deepened with the development of the disease. Small tissue samples collected from the infected lesions were surface-sterilized with 70% ethanol for 30 s, then rinsed with 2% sodium hypochlorite (NaClO) for 60 s, and finally rinsed with sterilized water three times. All the samples were transferred to potato dextrose agar (PDA) medium and incubated at 25℃ in the dark for 5 days (Zhu et al. 2013). A total of 9 isolates were obtained from the 11 selected tissues of symptomatic leaves. Afterward, all the single spore isolates were transferred onto potato carrot agar (PCA) plates (Mirkova 2003). After 7 to 10 days of incubation on PCA at 25℃ in the dark, colonies had a cottony mycelium with round margins, colored in white to gray. To test pathogenicity, six healthy G. pulchella plants were inoculated with mycelial plugs of the above pure cultures from a 7-day-old culture grown on PCA, while six germfree PCA plugs were served as negative controls. All the inoculated plants were set in greenhouse incubator at 25℃ and 80% relative humidity. Following 5 days incubation, brown spots began to appear on the sites of all inoculated leaves with mycelial plugs, while all the negative controls inoculated with sterile PCA plugs remained healthy. Infected lesions were separated and cultured as the same as those isolated in the field, and the same isolate was again microscopically identified, fulfilling Koch's postulates. 5 isolates were characterized, the colony margins of single spore isolate were round with gray or black aerial mycelia. Conidia were clustered and unbranched with 1 to 4 septa, colored in light or dark brown, shaped in obclavate or ellipsoid with short conical beak at the tip, dimensions varied from 14 to 51 μm (length) × 4.5 to 11 μm (width). The described morphological characteristics were consistent with Alternaria alternata (Simmons 2007). For further identification of molecular characterization, the genes of Chitin synthase (CHSD), RNA polymerase II second largest subunit (PRB2), Tsr1 ribosome biogenesis protein (Tsr1) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were obtained by PCR amplification with the primer pairs CHSDF1/CHSDR1, PRB2DF/PRB2DR, Tsr1F/Tsr1R and GAPDHF1/GAPDHR1 (Damn et al. 2019; Lawrence et al. 2013), respectively. The sequenced genes (GenBank accession nos. ON660874, ON660875, ON660876 and ON660877) had more than 99% nucleotide identity with the corresponding genes (GenBank accession nos. KY996470.1, MN304718.1, KY996472.1 and MN158133.1) of the reference strains of A. alternata in GenBank, and the re-inoculated and re-isolated strains have the same results which were repeated three times. The causal agent occurred on G. pulchella was identified as A. alternata based on the morphological and molecular characteristics. To our knowledge, this is the first record causing leaf spot on G. pulchella by A. alternata in China.

Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science

Multispectral technology has a wide range of applications in agriculture. By obtaining spectral information during crop production, key information such as growth, pests and diseases, fertilizer and pesticide application can be determined quickly, accurately and efficiently. The scientific analysis based on Web of Science aims to understand the research hotspots and areas of interest in the field of agricultural multispectral technology. The publications related to agricultural multispectral research in agriculture between 2002 and 2021 were selected as the research objects. The softwares of CiteSpace, VOSviewer, and Microsoft Excel were used to provide a comprehensive review of agricultural multispectral research in terms of research areas, institutions, influential journals, and core authors. Results of the analysis show that the number of publications increased each year, with the largest increase in 2019. Remote sensing, imaging technology, environmental science, and ecology are the most popular research directions. The journal Remote Sensing is one of the most popular publishers, showing a high publishing potential in multispectral research in agriculture. The institution with the most research literature and citations is the USDA. In terms of the number of papers, Mtanga is the author with the most published articles in recent years. Through keyword co-citation analysis, it is determined that the main research areas of this topic focus on remote sensing, crop classification, plant phenotypes and other research areas. The literature co-citation analysis indicates that the main research directions concentrate in vegetation index, satellite remote sensing applications and machine learning modeling. There is still a lot of room for development of multi-spectrum technology. Further development can be carried out in the areas of multi-device synergy, spectral fusion, airborne equipment improvement, and real-time image processing technology, which will cooperate with each other to further play the role of multi-spectrum in agriculture and promote the development of agriculture.

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi's effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model's mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R ² between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

Characteristics of unmanned aerial spraying systems and related spray drift: A review

Although drift is not a new issue, it deserves further attention for Unmanned Aerial Spraying Systems (UASS). The use of UASS as a spraying tool for Plant Protection Products is currently explored and applied worldwide. They boast different benefits such as reduced applicator exposure, high operating efficiency and are unconcerned by field-related constraints (ground slope, ground resistance). This review summarizes UASS characteristics, spray drift and the factors affecting UASS drift, and further research that still needs to be developed. The distinctive features of UASS comprise the existence of one or more rotors, relatively higher spraying altitude, faster-flying speed, and limited payload. This study highlights that due to most of these features, the drift of UASS may be inevitable. However, this drift could be effectively reduced by optimizing the structural layout of the rotor and spraying system, adjusting the operating parameters, and establishing a drift buffer zone. Further efforts are still necessary to better assess the drift characteristics of UASS, establish drift models from typical models, crops, and climate environments, and discuss standard methods for measuring UASS drift.

Assessing the application of spot spray in Nanguo pear orchards: Effect of nozzle type, spray volume rate and adjuvant

BACKGROUND

Aerial spray is one of the most important applications of unmanned aerial vehicles (UAVs) in agriculture. This work aimed to promote the use of UAVs as an alternative to knapsack electric sprayers in pesticide application in Nanguo pear orchards planted in mountain terraced orchard scenarios. The spray deposition of four types of nozzles (SX110015, XR80015, IDK90015 and TR80015), two spray volume rates (45 and 90 L ha^-1 ) and with or without a commercial surfactant adjuvant were evaluated based on the spot spray mode.

RESULTS

The air- assisted IDK90015 nozzle showed significantly higher deposition and penetration, and its large droplet size also reduced the risk of drift. Increasing the spray volume rate can increase the amount of droplets deposition. The adjuvant showed excellent potential to improve spray technology in Nanguo pear trees, with a mean deposition of 0.175-0.574 μL cm^-2 and penetration of 3.09-66.73%. The droplet size also increased significantly, with volume median diameter (D_V0.5 ) of 469 μm.

CONCLUSION

The nozzle type, spray volume rate and adjuvant should be well considered when using the spot spray in orchard. Compared with increasing spray volume rate, the use of air-induction nozzles and surfactant-based adjuvants can improve the spray deposition better. © 2022 Society of Chemical Industry.

Droplet deposition and pest control efficacy on pine trees from aerial application

BACKGROUND

Aerial application, a spraying method with aircraft as the application platform, is one of the most important methods of pest control in pine forests. This work aimed to investigate the movement and distribution patterns of droplets and the pest control effectiveness of helicopter spraying on Masson pine (MP) and Korean pine (KP) trees. In particular, three nozzle types (CP02, CP03 and CP04) were evaluated and compared at the same spray volume rate (15 L ha^-1 ) in both wind tunnel and field trials.

RESULTS

The CP04 nozzle with a larger orifice size showed significantly better deposition and penetration, but the precision of flight operation parameters must be strictly controlled when using this type of nozzle. The majority (≥48%) of the droplets produced by the three nozzle types were concentrated in the range of <200 μm, and the spray performance of each nozzle type was more stable in the range 201-300 μm. The final pest correction control rate (CR) of MP was in the range 59.24-95.74% and that of KP was 45.41-54.39% by helicopter spraying.

CONCLUSION

This study confirmed that it is necessary to select the appropriate nozzle type and ensure accurate flight parameters for aerial application in mountain pine forests. Meanwhile, it also confirms the importance of the timing of operation which can lead to incorrect implementation, resulting in poor control effectiveness. © 2022 Society of Chemical Industry.

MSX1 Drives Tooth Morphogenesis Through Controlling Wnt Signaling Activity

Tooth agenesis is a common structural birth defect in humans that results from failure of morphogenesis during early tooth development. The homeobox transcription factor Msx1 and the canonical Wnt signaling pathway are essential for "bud to cap" morphogenesis and are causal factors for tooth agenesis. Our recent study suggested that Msx1 regulates Wnt signaling during early tooth development by suppressing the expression of Dkk2 and Sfrp2 in the tooth bud mesenchyme, and it demonstrated partial rescue of Msx1-deficient molar teeth by a combination of DKK inhibition and genetic inactivation of SFRPs. In this study, we found that Sostdc1/Wise, another secreted Wnt antagonist, is involved in regulating the odontogenic pathway downstream of Msx1. Whereas Sostdc1 expression in the developing tooth germ was not increased in Msx1-/- embryos, genetic inactivation of Sostdc1 rescued maxillary molar, but not mandibular molar, morphogenesis in Msx1-/- mice with full penetrance. Since the Msx1-/-;Sostdc1-/- embryos exhibited ectopic Dkk2 expression in the developing dental mesenchyme, similar to Msx1-/- embryos, we generated and analyzed tooth development in Msx1-/-;Dkk2-/- double and Msx1-/-;Dkk2-/-;Sostdc1-/- triple mutant mice. The Msx1-/-;Dkk2-/- double mutants showed rescued maxillary molar morphogenesis at high penetrance, with a small percentage also exhibiting mandibular molars that transitioned to the cap stage. Furthermore, tooth development was rescued in the maxillary and mandibular molars, with full penetrance, in the Msx1-/-;Dkk2-/-;Sostdc1-/- mice. Together, these data reveal 1) that a key role of Msx1 in driving tooth development through the bud-to-cap transition is to control the expression of Dkk2 and 2) that modulation of Wnt signaling activity by Dkk2 and Sostdc1 plays a crucial role in the Msx1-dependent odontogenic pathway during early tooth morphogenesis.

Real-Time Assessment of Mandarin Crop Water Stress Index

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

Effects of temperature and humidity on the contact angle of pesticide droplets on rice leaf surfaces

The effects of external factors such as temperature, humidity, pesticide formulation, and pesticide concentration on the contact angle of pesticide droplets on rice leaf surfaces were analyzed. The experiments showed that there were significant differences in the contact angles of droplets on the leaf surfaces under different temperatures and humidity. As the ambient temperature increased, the contact angle first decreased and then increased, reaching a minimum value at 25°C. With a gradual increase in humidity, the contact angle significantly increased and reached a maximum at 100% humidity. Finally, it was concluded that both the formulation and concentration of the pesticide had a significant effect on the contact angle of droplets on rice leaf surfaces. The experiments also illustrated that the effects of the pesticide formulation and concentration on the contact angle were more significant than those of temperature and humidity.

First Report of Alternaria alternata Causing Leaf Spot on Bellis perennis in China

Bellis perennis L., commonly known as the daisy or sun chrysanthemum, belonging to the family Asteraceae, is a perennial herb and is usually used as an ornamental plant worldwide for its vibrant flowers. Simultaneously, B. perennis has been proved to have therapeutic effects used on common colds, wound healing, anti-tumor, anxiolytic and antioxidant (Karakas et al. 2017). In July 2021, typical leaf spot was observed on B. perennis with about 50% disease incidence in Ruyue lake wetland park of Zibo (36.71°N, 118.01°E), Shandong Province, China. We surveyed more than 1000 square meters of planting area, and the diseased leaves were mostly concentrated in the lower location of plants, where the humidity was higher under the forest. Symptoms on the initially diseased leaves appeared as light yellow, round or oval lesions with light or brown borders. With the development of the disease, the area of the lesion gradually expands, the color deepens, and the shape is becoming irregular. To identify the causal pathogen, small pieces of 15 tissues collected from the infected leaves were sterilized with 75% ethanol for 30 s and then 2% sodium hypochlorite (NaClO) for 60 s, finally rinsed with sterile water three times. All the tissues were placed on potato dextrose agar (PDA) and incubated at 25 ℃ in the dark for 5 days (Zhu et al. 2013). A total of 13 isolates were obtained from the above diseased leaves. The cultures were initially grayish white, then a light green halo appeared in the middle of the medium after 5 days, with numerous gray aerial hyphae. For molecular identification, the RNA polymerase II beta subunit (PRB2), Tsr ribosome biogenesis protein, partial coding sequences of chitin synthase, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and major allergen Alt a 1 were amplified from genomic DNA extracted from four representative single isolates using the primers PRB2DF/PRB2DR, Tsr1F/Tsr1R, CHSDF1/CHSDR1, GDF1/GDR1, and AltF/AltR (Damn et al. 2019; Lawrence et al. 2013), respectively, and sequenced (GenBank accession nos. OL416000, OL416001, OL416002, OL416003, and OL416004). These genes had more than 99.9% nucleotide identity with the corresponding sequences (KY131957.1, KY131958.1, KY996470.1, MN657411.1, and KY923227.1) of the reference strains of Alternaria alternata in GenBank. For pathogenicity tests, five healthy B. perennis plants each with three living leaves were inoculated with mycelial plugs of A. alternata from a 5-day-old culture grown on PDA. After inoculation, the plants were placed in a greenhouse with 85% relative humidity at 25 ℃ and monitored daily for symptom development. After 3 days, all inoculated leaves with mycelial plugs of A. alternata appeared symptoms similar to those observed in the field previously, while no symptoms appeared on negative controls which were inoculated with sterile PDA plugs. Cultures re-isolated from diseased leaves had the same morphological and molecular results as those isolated in the field, confirming Koch's postulates. The causal agent on B. perennis was confirmed as A. alternata on the basis of morphological and molecular results (Simmons 2007). To our knowledge, this is the first report on the presence of A. alternata affecting B. perennis plants in China. The discovery of this new disease is beneficial to the application and protection of B. perennis, which is a popular landscape and medicinal plant.

UAV spraying on citrus crop: impact of tank-mix adjuvant on the contact angle and droplet distribution

Adding tank-mix adjuvants into the spray mixture is a common practice to improve droplet distribution for field crops (e.g., rice, wheat, corn, etc.) when using Unmanned Aerial Vehicle (UAV) sprayers. However, the effectiveness of tank-mix adjuvant for UAV spraying in orchard crops is still an open problem, considering their special canopy structure and leaf features. This study aims to evaluate the effects of a typical tank-mix adjuvant concentrations (i.e., Nong Jian Fei (NJF)) on Contact Angle (CA) and droplet distribution in the citrus tree canopy. Three commonly used parameters, namely dynamic CA, droplet coverage, and Volume Median Diameter (VMD), are adopted for performance evaluation. The dynamic CAs on the adaxial surface of citrus leaves, for water-only and NJF-presence sprays, respectively, are measured with five concentration levels, where three replications are performed for each concentration. The sprays with 0.5‰ NJF are adopted in the field experiment for evaluating droplet distributions, where Water Sensitive Papers (WSPs) are used as collectors. Two multi-rotor UAVs (DJI T20 and T30) which consist of different sizes of pesticide tanks and rotor diameters are used as the spraying platforms. Both water-only and NJF-presence treatments are conducted for the two UAVs, respectively. The results of the CA experiment show that NJF addition can significantly reduce the CAs of the sprays. The sprays with 0.5‰ NJF obtain the lowest CA within the observing time, suggesting a better spread ability on solid surface (e.g., WSPs or/and leaves). With respect to the effects of NJF addition on individual UAVs, the field trial results indicate that NJF addition can remarkably increase both the droplet coverage and VMD at three canopy layers, except for T30 droplet coverage of the inside and bottom layers. Comparing the difference of droplet coverage between two UAVs, while significant difference is found in the same layer before NJF addition, there is no notable difference appearing in the outside and bottom layers after NJF addition. The difference of VMD in the same layer between two UAVs is not affected by NJF addition except for the bottom layer. These results imply that the differences of droplet coverage among different UAVs might be mitigated, thus the droplet distribution of some UAVs could be improved by adding a tank-mix adjuvant into the sprays. This hypothesis is verified by investigating the droplet penetration and the correlation coefficient (CC) of droplet coverage and VMD. After NJF addition, the total percentage of T20 droplet coverage in the bottom and inside layers is increased by 5%. For both UAVs, the CCs indicate that both droplet coverage and VMD increase at the same time in most cases after NJF addition. In conclusion, the addition of a tank-mix adjuvant with the ability to reduce CA of the sprays, can effectively improve droplet distribution using UAV spraying in the citrus canopy by increasing droplet coverage and VMD.

Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging

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Predicting the concentration of polysaccharides by hyperspectral images of the Ganoderma lucidum cap is feasible.

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Establishing calibration models using visible and near-infrared spectroscopy respectively to find out the characteristic spectrum.

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Exploring the influence of different tissue parts on prediction through ROI selection.

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Prediction of polysaccharide concentration in the full life cycle of the Ganoderma lucidum fruiting body.

Ganoderma lucidum is a traditional Chinese healthy food with many kinds of nutritious activities, and polysaccharide is one of its main active components. Ganoderma lucidum polysaccharide plays a vital role in improving human immunity and anti-oxidation. At present, the methods of detecting polysaccharide content of Ganoderma lucidum are destructive, and the steps are complicated and time-consuming. This study aims to explore the possibility of using hyperspectral imaging (HSI) to predict polysaccharide content in a nondestructive way during the growth of Ganoderma lucidum. The partial least square regression (PLSR) model shows good performance for Ganoderma lucidum (Rp2 = 0.924, RPDp = 3.622) with pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and feature selection method of successive projections algorithm (SPA). This study indicates that HSI can quickly and nondestructive detect the polysaccharide content of Ganoderma lucidum, provide guidance for the cultivation industry and improve the economic benefits of Ganoderma lucidum.

Detection Method of Citrus Psyllids With Field High-Definition Camera Based on Improved Cascade Region-Based Convolution Neural Networks

Citrus psyllid is the only insect vector of citrus Huanglongbing (HLB), which is the most destructive disease in the citrus industry. There is no effective treatment for HLB, so detecting citrus psyllids as soon as possible is the key prevention measure for citrus HLB. It is time-consuming and laborious to search for citrus psyllids through artificial patrol, which is inconvenient for the management of citrus orchards. With the development of artificial intelligence technology, a computer vision method instead of the artificial patrol can be adopted for orchard management to reduce the cost and time. The citrus psyllid is small in shape and gray in color, similar to the stem, stump, and withered part of the leaves, leading to difficulty for the traditional target detection algorithm to achieve a good recognition effect. In this work, in order to make the model have good generalization ability under outdoor light condition, a high-definition camera to collect data set of citrus psyllids and citrus fruit flies under natural light condition was used, a method to increase the number of small target pests in citrus based on semantic segmentation algorithm was proposed, and the cascade region-based convolution neural networks (R-CNN) (convolutional neural network) algorithm was improved to enhance the recognition effect of small target pests using multiscale training, combining CBAM attention mechanism with high-resolution feature retention network high-resoultion network (HRNet) as feature extraction network, adding sawtooth atrous spatial pyramid pooling (ASPP) structure to fully extract high-resolution features from different scales, and adding feature pyramid networks (FPN) structure for feature fusion at different scales. To mine difficult samples more deeply, an online hard sample mining strategy was adopted in the process of model sampling. The results show that the improved cascade R-CNN algorithm after training has an average recognition accuracy of 88.78% for citrus psyllids. Compared with VGG16, ResNet50, and other common networks, the improved small target recognition algorithm obtains the highest recognition performance. Experimental results also show that the improved cascade R-CNN algorithm not only performs well in citrus psylla identification but also in other small targets such as citrus fruit flies, which makes it possible and feasible to detect small target pests with a field high-definition camera.

Effects of nitrogen supply rate on photosynthesis, nitrogen uptake and growth of seedlings in a Eucalyptus/Dalbergia odorifera intercropping system

The introduction of N₂ -fixing species into a Eucalyptus plantation resulted in a successful planting system. It is essential to understand the contribution of nitrogen (N) competition and photosynthetic efficiency to plant dry matter yield to shed more light on the growth mechanism of the Eucalyptus/legume system. We compared N competition, photosynthesis and dry matter yield of Eucalyptus urophylla × E. grandis and the N₂ -fixing tree species Dalbergia odorifera in intercropping and monoculture systems under different N levels. The photosynthesis of E. urophylla × E. grandis was improved, while that of D. odorifera was inhibited in the intercropping system. Intercropped E. urophylla × E. grandis increased the N utilization and the dry matter yield by 6.57-48.46% and 7.59-97.26%, and decreased those of D. odorifera by 10.21-30.33% and 0.48-13.19%, respectively. Furthermore, N application enhanced the competitive ability of E. urophylla × E. grandis relative to D. odorifera and changed the N contents and chlorophyll synthesis to optimize the photosynthetic structure of both species. Our results reveal Eucalyptus for photosynthesis, N absorption and increasing the growth benefit from the introduction of N₂ -fixing species, which hence can be considered to be an effective sustainable management option of Eucalyptus plantations.

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

The precision detection of dense small targets in orchards is critical for the visual perception of agricultural picking robots. At present, the visual detection algorithms for plums still have a poor recognition effect due to the characteristics of small plum shapes and dense growth. Thus, this paper proposed a lightweight model based on the improved You Only Look Once version 4 (YOLOv4) to detect dense plums in orchards. First, we employed a data augmentation method based on category balance to alleviate the imbalance in the number of plums of different maturity levels and insufficient data quantity. Second, we abandoned Center and Scale Prediction Darknet53 (CSPDarknet53) and chose a lighter MobilenetV3 on selecting backbone feature extraction networks. In the feature fusion stage, we used depthwise separable convolution (DSC) instead of standard convolution to achieve the purpose of reducing model parameters. To solve the insufficient feature extraction problem of dense targets, this model achieved fine-grained detection by introducing a 152 × 152 feature layer. The Focal loss and complete intersection over union (CIOU) loss were joined to balance the contribution of hard-to-classify and easy-to-classify samples to the total loss. Then, the improved model was trained through transfer learning at different stages. Finally, several groups of detection experiments were designed to evaluate the performance of the improved model. The results showed that the improved YOLOv4 model had the best mean average precision (mAP) performance than YOLOv4, YOLOv4-tiny, and MobileNet-Single Shot Multibox Detector (MobileNet-SSD). Compared with some results from the YOLOv4 model, the model size of the improved model is compressed by 77.85%, the parameters are only 17.92% of the original model parameters, and the detection speed is accelerated by 112%. In addition, the influence of the automatic data balance algorithm on the accuracy of the model and the detection effect of the improved model under different illumination angles, different intensity levels, and different types of occlusions were discussed in this paper. It is indicated that the improved detection model has strong robustness and high accuracy under the real natural environment, which can provide data reference for the subsequent orchard yield estimation and engineering applications of robot picking work.

Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

Citrus Huanglongbing (HLB), also named citrus greening disease, occurs worldwide and is known as a citrus cancer without an effective treatment. The symptoms of HLB are similar to those of nutritional deficiency or other disease. The methods based on single-source information, such as RGB images or hyperspectral data, are not able to achieve great detection performance. In this study, a multi-modal feature fusion network, combining a RGB image network and hyperspectral band extraction network, was proposed to recognize HLB from four categories (HLB, suspected HLB, Zn-deficient, and healthy). Three contributions including a dimension-reduction scheme for hyperspectral data based on a soft attention mechanism, a feature fusion proposal based on a bilinear fusion method, and auxiliary classifiers to extract more useful information are introduced in this manuscript. The multi-modal feature fusion network can effectively classify the above four types of citrus leaves and is better than single-modal classifiers. In experiments, the highest accuracy of multi-modal network recognition was 97.89% when the amount of data was not very abundant (1,325 images of the four aforementioned types and 1,325 pieces of hyperspectral data), while the single-modal network with RGB images only achieved 87.98% recognition and the single-modal network using hyperspectral information only 89%. Results show that the proposed multi-modal network implementing the concept of multi-source information fusion provides a better way to detect citrus HLB and citrus deficiency.

CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study

AIM

To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma.

MATERIALS AND METHODS

A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated.

RESULTS

Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867-0.914) and 0.864 (95% CI: 0.817-0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively).

CONCLUSIONS

A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making.

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.

Feature Distillation in Deep Attention Network Against Adversarial Examples

Deep neural networks (DNNs) are easily fooled by adversarial examples. Most existing defense strategies defend against adversarial examples based on full information of whole images. In reality, one possible reason as to why humans are not sensitive to adversarial perturbations is that the human visual mechanism often concentrates on most important regions of images. A deep attention mechanism has been applied in many computer fields and has achieved great success. Attention modules are composed of an attention branch and a trunk branch. The encoder/decoder architecture in the attention branch has potential of compressing adversarial perturbations. In this article, we theoretically prove that attention modules can compress adversarial perturbations by destroying potential linear characteristics of DNNs. Considering the distribution characteristics of adversarial perturbations in different frequency bands, we design and compare three types of attention modules based on frequency decomposition and reorganization to defend against adversarial examples. Moreover, we find that our designed attention modules can obtain high classification accuracies on clean images by locating attention regions more accurately. Experimental results on the CIFAR and ImageNet dataset demonstrate that frequency reorganization in attention modules can not only achieve good robustness to adversarial perturbations, but also obtain comparable, even higher classification, accuracies on clean images. Moreover, our proposed attention modules can be integrated with existing defense strategies as components to further improve adversarial robustness.

Stoichioproteomics study of differentially expressed proteins and pathways in head and neck cancer

Hypoxia is a prominent feature of head and neck cancer. However, the oxygen element characteristics of proteins and how they adapt to hypoxia microenvironments of head and neck cancer are still unknown. Human genome sequences and proteins expressed data of head and neck cancer were retrieved from pathology atlas of Human Protein Atlas project. Then compared the oxygen and carbon element contents between proteomes of head and neck cancer and normal oral mucosa-squamous epithelial cells, genome locations, pathways, and functional dissection associated with head and neck cancer were also studied. A total of 902 differentially expressed proteins were observed where the average oxygen content is higher than that of the lowly expressed proteins in head and neck cancer proteins. Further, the average oxygen content of the up regulated proteins was 2.54% higher than other. None of their coding genes were distributed on the Y chromosome. The up regulated proteins were enriched in endocytosis, apoptosis and regulation of actin cytoskeleton. The increased oxygen contents of the highly expressed and the up regulated proteins might be caused by frequent activity of cytoskeleton and adapted to the rapid growth and fast division of the head and neck cancer cells. The oxygen usage bias and key proteins may help us to understand the mechanisms behind head and neck cancer in targeted therapy, which lays a foundation for the application of stoichioproteomics in targeted therapy and provides promise for potential treatments for head and neck cancer.