Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Research on Individual Tree Canopy Segmentation of <i>Camellia oleifera</i> Based on a UAV-LiDAR System

In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields

Monitor Cotton Budding Using SVM and UAV Images

Monitoring the cotton budding rate is important for growers so that they can replant cotton in a timely fashion at locations at which cotton density is sparse. In this study, a true-color camera was mounted on an unmanned aerial vehicle (UAV) and used to collect images of young cotton plants to estimate the germination of cotton plants. The collected images were preprocessed by stitching them together to obtain the single orthomosaic image. The support-vector machine method and maximum likelihood classification method were conducted to identify the cotton plants in the image. The accuracy evaluation indicated the overall accuracy of the classification for SVM is 96.65% with the Kappa coefficient of 93.99%, while for maximum likelihood classification, the accuracy is 87.85% with a Kappa coefficient of 80.67%. A method based on the morphological characteristics of cotton plants was proposed to identify and count the overlapping cotton plants in this study. The analysis showed that the method can improve the detection accuracy by 6.3% when compared to without it. The validation based on visual interpretation indicated that the method presented an accuracy of 91.13%. The study showed that the minimal resolution of no less than 1.2 cm/pixel in practice for image collection is necessary in order to recognize cotton plants accurately

Algorithm for Extracting the 3D Pose Information of <i>Hyphantria cunea</i> (Drury) with Monocular Vision

Currently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep learning models. In this study, an algorithm of the 3D posture information extraction method for Hyphantria cunea (Drury) based on monocular vision is proposed. Four images of every collected sample of H. cunea were taken at 90° intervals. The 3D pose information of the wings was extracted using boundary tracking, edge fitting, precise positioning and matching, and calculation. The 3D posture information of the torso was obtained by edge extraction and curve fitting. Finally, the 3D posture information of the wings and abdomen obtained by this method was compared with that obtained by Metrology-grade 3D scanner measurement. The results showed that the relative error of the wing angle was between 0.32% and 3.03%, the root mean square error was 1.9363, and the average relative error of the torso was 2.77%. The 3D posture information of H. cunea can provide important data support for sample augmentation and species identification of moth pests

脉宽调制变量控制喷头雾化性能及风洞环境雾滴沉积特性

Pulse width modulation (PWM) technology is an important means to achieve variable spray, and is attracting more and more researchers' attention due to its short reaction time, fast response, large flow adjustment range and good spray characteristics using conventional nozzles. But during the actual spraying process, its working parameters and environmental conditions could influence the spray accuracy seriously. In order to investigate the atomization and deposition characteristics of the PWM variable-rate nozzle, a pulse width modulation variable spraying system was designed to study the spraying atomization and deposition characteristics of TR80-005C hollow cone spray nozzle commonly used in agricultural application. In order to maintain a stable environmental condition to produce setting wind speed, the experiments were carried out in the type IEA-II wind tunnel designed by Agricultural Intelligent Equipment Technology Research Center. A dot matrix placement capacitive droplet deposition monitoring sensor was used to detect spray deposition in real time. To effectively evaluate the ground deposition properties of the wind tunnel environment, deposition evaluation index (DEIX) was deduced based on drift potional index (DIX). DEIX is inversely proportional to DIX, the smaller the DEIX value, the smaller the potential of droplet deposition, which means that the possibility of drift loss is greater. The Spraytec droplet size meter was sued to test the droplet volume median diameter (VMD) and the relative span of the droplets (RS) to determine the relationship between duty cycle and spraying atomization performance. The experiment was carried out at the Xiaotangshan National Precision Agriculture Research Station in Changping district of Beijing city. The test devices were mainly composed of PWM variable-rate spraying system, IEA-II conventional-speed wind tunnel, laser particle size analyzer and deposition measurement sensor network system. Before tests, all systems were powered on and warm up for 30 minutes. In the droplet size tests, the nozzle was placed 0.5 m directly above the droplet size analyzer, the test pressure was set to 0.4 MPa, the PWM frequency was set to 1 Hz, and the duty cycle was set to 10%-60% at the interval of 10%. Tap water was used as the spraying solution, and each setting repeated 5 times. For the droplet deposition characteristic tests, droplet deposition sensor was arranged at the bottom of the wind tunnel, the sensors were arranged in 5 rows (1 m spacing) and 3 columns (0.55 m spacing) on the vertical wind direction, and were numbered 1 to 15 starting from the upper side to the bottom of the upper side. The nozzle was fixed at the top of the wind tunnel, and the height of the relative deposition sensor was set to 1and 1.5 m respectively, and the horizontal distance between the nozzle and the first column deposition sensor was 1.3 m, the wind speed was set to 1-5 m/s, PWM frequency was set to 1 Hz and duty cycle was 10%-60%, spraying time was set as 10 s, the spraying pressure was set as 0.4 MPa. At the beginning of the test, the sensors saved datas in real time and transmitted it back to the computer. The test results showed that when the duty cycle was between 10%-40%, the VMD decreased with the increases of duty cycle, VMD was 122.3 μm at 60% duty cycle, which increased by 1.8 μm compared with that of at 40% duty cycle. When the PWM duty cycle was 60%, the RS was the smallest, compared with that of duty cycle at 20%, the RS decreased by 9.52%, that means that the droplet spectrum was the narrowest, and droplet size distribution was the most concentrated. In the deposition test, under the condition of wind speed at 1 m/s, droplets were mainly deposited within 3.3 m from the nozzle, which accounted for 95.7% of the total deposition. When the wind speed exceeded 3 m/s, the droplet settling distance increased under the action of the airflow, which may increase the possibility of spray drift. With increase of the duty cycle, DEIX value decreased and the drift rate of the droplets increased. Under the same working conditions, the larger the wind speed and the nozzle height, the smaller the DEIX and the easier spray drift. This study provides a basis for the practical application of pulse width modulation variable application techniques and PWM working condition parameters selection in agricultural field production, and provides a theoretical basis for further optimization of PWM variable adjustment devices

Modeling and Experimental Validation of the Atomization Efficiency of a Rotary Atomizer for Aerial Spraying

Rotary atomizers are mainly used in agricultural manned aircrafts. Atomization characteristics at high speeds have been studied, but methods to measure the atomization efficiency have not been elucidated. The atomization efficiency of rotary atomizers under high-speed airflow was investigated using an IEA-I high-speed wind tunnel experimental installation, AU5000 rotary atomizer, and a laser diffraction particle size analyzer. Accordingly, a model equation for atomization efficiency measurements was innovatively obtained. When the flow rate, fan blade angle of the atomizer, and wind speed were used as variables, the experimental results showed that the atomization efficiency mainly depended on the fan blade angle. When the fan blade angle was 35°, the atomization efficiency was optimal, regardless of wind speed. In contrast, when the fan blade angle of the atomizer was 65°, it exhibited the worst atomization efficiency, regardless of the wind speed. The experimental data from this study can provide guidance for aerial application in fixed-wing manned aircraft, such as the flow rate, and operating speed

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models

A new spray deposition pattern measurement system based on spectral analysis of a fluorescent tracer

To complement shortages of discrete sampling data and improve the detection accuracy of droplet deposition in unmanned aerial vehicle (UAV) spraying, we developed a new spray deposition pattern measurement system (SDPMS) based on a fluorescent tracer and spectral analysis. Then, we evaluated the system performance in two field spraying experiments in comparison with water-sensitive paper results. The system comprises a fluorescence scanner and spectral analysis program. The fluorescence scanner includes an ultraviolet light, spectrometer, far end controller, stepping motor, and sample reel. First, 1.0% fluorescent tracer solution is sprayed, and the droplets are collected on a paper strip. Then, the paper strip is scanned with the fluorescence scanner, and a set of fluorescence intensity values is collected and processed by the spectral analysis program. Finally, the spray deposition pattern is calculated. The experimental results showed that the spray deposition pattern from the SDPMS had a 0.89 correlation coefficient with that of water-sensitive paper. A linear regression model between fluorescence intensity and deposit coverage was constructed, with a coefficient of determination of 0.91 (F = 61.8845, P < 0.001). In addition, a linear regression model between fluorescence intensity and volume rate was constructed, with a coefficient of determination of 0.89 (F = 51.6639, P < 0.001). The SDPMS and field experiments offer a good foundation for the development of an improved system compatible with UAV spraying

Development of a Low-Power Automatic Monitoring System for <i>Spodoptera frugiperda</i> (J. E. Smith)

Traditional traps for Spodoptera frugiperda (J. E. Smith) monitoring require manual counting, which is time-consuming and laborious. Automatic monitoring devices based on machine vision for pests captured by sex pheromone lures have the problems of large size, high power consumption, and high cost. In this study, we developed a micro- and low-power pest monitoring device based on machine vision, in which the pest image was acquired timely and processed using the MATLAB algorithm. The minimum and maximum power consumption of an image was 6.68 mWh and 78.93 mWh, respectively. The minimum and maximum days of monitoring device captured image at different resolutions were 7 and 1486, respectively. The optimal image resolutions and capture periods could be determined according to field application requirements, and a micro-solar panel for battery charging was added to further extend the field life of the device. The results of the automatic counting showed that the counting accuracy of S. frugiperda was 94.10%. The automatic monitoring device had the advantages of low-power consumption and high recognition accuracy, and real-time information on S. frugiperda could be obtained. It is suitable for large-scale and long-term pest monitoring and provides an important reference for pest control