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Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Diabetes Mellitus is the property of Chinese Journal of Diabetes Mellitus and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing application of plant protection unmanned aerial vehicle (UAV) in precision agriculture, the numerical simulation methods for the development of the downwash flow field of the plant protection UAV and the deposition and drift process of droplets affected by the downwash flow field have achieved rapid and diversified development, but the advantages, disadvantages, scope of application, and verification of each method still lack a systematic review. This article discusses the inviscid model, computational fluid dynamics model and lattice Boltzmann model (LBM) respectively. The advantage of the inviscid wake vortex model based on the vortex element method is that the calculation process is simple. Moreover, integrated with the most widely used aerial spray drift prediction software AGricultural DISPersal (AGDISP), it can be a promising way to do real-time UAV spray drift prediction. But due to lack of viscosity and turbulence models, the droplet deposition and drift simulation accuracy of inviscid model is relatively lower than other models. The computational fluid dynamics (CFD) model includes the finite volume method (FVM) and the finite difference method (FDM). The FVM in the computational fluid dynamics model has high robustness and can be applied to the simulation of various complex environments. Many commercial CFD software are based on FVM and achieved a fast development in aerial spray modeling recently. However, the FVM is greatly affected by the quality of the mesh, and its commonly used upwind style has limited accuracy (second-order accuracy). Under the same mesh density, it is easier to generate artificial dissipation when simulating the rotor tip vortex than the finite difference method. As a result, the simulated rotor tip vortex dissipation speed is much faster than the actual situation. Compared with the FVM, the structured grid used in the FDM is easier to construct a high-order precision numerical format. Which can reach 4-5 orders of accuracy, and with adaptive grid technology, FDM can simulate the evolution of rotor tip vortex with high temporal and spatial accuracy, and can reproduce the typical flow structure development process of the real rotor downwash flow field. However, it also has problems such as high grid structure requirements and excessive computing power requirements. LBM has advantages in computing three-dimensional flow field problems with complex boundary conditions and non-stationary moving objects. However, there are still shortcomings in its functional diversity and completeness. The accuracy of the numerical models mentioned above still needs field test and indoor experiment such as high-speed Particle Image Velocimetry (PIV)/ Phase Doppler Interferometry (PDI) method to verify and optimize. The authors finally pointed out the future direction of plant protection UAV application simulation and verification. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle(UAV) application in agriculture and forestry has the unique advantages of high efficiency, water and pesticide saving, and strong adaptability to complex terrain. The application research of UAV in agriculture and forestry has shown a fast growing trend. In order to explore the research hotspots and the scientific impact of countries/regions and institutions on UAV application in agriculture and forestry, the relevant literatures in the Web of Science(WoS) core collection database (2011-2020) were collected. The bibliometrics analysis was performed on the journal articles of UAV application in agriculture and forestry based on VOSviewer, WoS analysis tools and Microsoft Excel. The analysis results showed that the number of published papers increased rapidly since 2017, the researches on UAV application in agriculture and forestry were carried out in 94 countries/regions, including1778 institutions. Due to the strong scientific research group in the application of UAV in agriculture and forestry of the United States, China and Australia, a large number of papers had been published, resulting in a great academic influence. Remote sensing was the most widely used technology field of UAV application in agriculture and forestry, mainly involving remote sensing technology, ecological environment science, image processing technology, geological science, etc. Engineering was an important technical field of UAV application in agriculture and forestry, mainly involving control technology, sensor technology and fluid computing modeling technology related to UAV aerial pesticide application.There were 1508 articles and reviews been published in 398 journals, about 1.90% of all journals included in WoS core collection database, indicating that more and more journals paid attention to the application research of UAV in agriculture and forestry. Remote Sensing sponsored by MDPI (Multidisciplinary Digital Publishing Institute) was the journal that published the most of papers, the most cited paper mainly focused on the research status of UAV system in photogrammetry and remote sensing, including sensing, navigation, positioning and general data processing, etc. In addition, the analysis of the research hotspots of UAV application in agriculture and forestry showed that UAV pesticide application, UAV remote sensing of diseases and pests, plant phenotype acquisition were the research hotspots. This study can provide references for innovation research and cooperation between research teams of UAV application in agriculture and forestry. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Science Research & Application is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pine wilt nematode is a species of nematode that infects pine trees and causes the pine wilt disease. The affected pine will infect other pines in the short term, hence, identification and removal of the affected pines are important to control the disease. In this study, a fix-wing Unmanned Aerial Vehicle (UAV) equipped with a professional true-color camera was used to collect images for the study area, and a deep learning network, the U-Net model, was adopted to segment the images of wilt pines. Several 3 799 wilt pine images in this study were collected by the UAV, of which, 1 567 images for the Heihu mountain area and 2 232 images for Yangkou tunnel. The ground survey was conducted to collect the true wilt pines, and 45 samples were obtained. The collected images were identified by visual interpretation, and compared with the ground true samples, the accuracy of the visual interpretations was 95.24%. Label images were generated manually according to the visual interpretation results, and the label images amplification method was adopted to get more training samples. The training samples of 1 3120 were obtained in the study, and the training was performed using a GTX 2080 Ti GPU with a memory of 11GB. The training accuracy gradually increased as the number of iterations increased, and the training loss value gradually decreased as the number of iterations increased. The training and validation accuracies of the U-Net model were 98.74% and 97.76% respectively, and the problems such as over-fitting or under-fitting and gradient disappearance were not observed in the training. The confusion matrix was used to evaluate the segmentation accuracy of the U-Net model, and 145 images were randomly selected to perform the confusion matrix. The confusion matrix showed that the user and producer accuracies for the wilt wood segmentation were 93.51% and 97.30% respectively, the false positive rate was 6.49%, and the false-negative rate was 2.70%. This indicated the accuracy of the U-Net model achieved the accuracy requirements of segmentation wilt pine which required both a low false-positive rate and false-negative rate but emphasized more on the lower false-negative rate. In general, the overall segmentation accuracy of the U-Net model is 95.17% with the Kappa coefficient of 0.9. The accuracy evaluation indicated the segmentation model achieved a reasonable accuracy. A comparison with the Random Forest (RF) algorithm was also conducted to evaluate the performance difference between the RF and the U-Net model, the accuracy analysis showed that the U-Net network greatly reduced the false positive rate and the noise of segmentation compared with the random forest method. The analysis also indicated the U-Net model achieved higher accuracy than the RF model. The classification results of the RF model without using the texture information which includes Local Binary Pattern (LBP), angular second moment, contrast, correlation, variance, entropy, mean, and homogeneity, presented heavy salt and pepper noise. Although the texture information was useful for the RF method, it still showed low accuracy compared with the U-Net model. To analyze the effects of the number of samples and the quality of samples on model accuracy, a comparative analysis was conducted by reducing the training samples and degradation of the quality of the training samples. The number of training samples was reduced by 2/3, that is, 4 373 samples were randomly selected to train the model. On the other hand, a wilt wood mask with a size of 1-20 pixels was randomly added to the 50% labels for using to train the model. The results indicated that the quality of the samples was more important for the U-Net network to identify the wilt pine woods. In general, the U-Net deep learning segmentation network is suitable for monitoring wilt pine woods in large areas. [ABSTRACT FROM AUTHOR]

With the development of agricultural aviation technologies and their application in agricultural production, plant protection unmanned aerial vehicle (UAV) has been widely used to control pests and diseases of crops. The high speed rotation of the rotor in the UAV produces a powerful downwash affecting the distribution of pesticide droplets on the ground. Understanding spatial distribution of these droplets on the ground is important to evaluate application quality of the pesticides and plays an important role in improving the spray system in UAV and optimizing its operating parameters. Current methods for measuring the droplet deposition distributions use a number of collectors placed regularly on the ground to receive the droplets and measured their sizes; it is difficult for them to effectively obtain the deposition of all droplets result due to the downwash of UAV. This paper presents a new method to resolve this problem by improving accuracy and spatial continuity of pesticide droplets measurement applied by an unmanned helicopter. The flying parameters of a 3WQF-80-10 unmanned helicopter used to spray pesticides were obtained from the high-precision Beidou navigation system, and the RQT-C-3 fluorescent whitening tracer with mass fraction of 1.0% was used as the proxy for the pesticides. Two droplet collection methods: one used continuous strip paper and the other one used individual water sensitive paper, were used to measure the droplets deposition distribution. We divided the experimental field into three areas, with Areas 1 and 2 spaced 3 m apart, and Areas 2 and 3 spaced 1m apart. A metal bracket 8 m log and 0.5 m away from the ground was placed in each area. Prior to the experiment, a paper tape was fixed on the surface of the bracket and the water-sensitive paper cards were placed evenly in the area 0.5 m away from the paper tape. There were one paper tape and 15 water sensitive papers in each area, and a total of six spray tests were performed based on pro-designed flight parameters. The combinations of flight speed and flight height were: 2 m/s and 3 m, 2 m/s and 6 m, 2 m/s and 9 m, 3 m/s and 3 m, 3 m/s and 6 m, and 4 m/s and 9 m. The paper tape was detected by fluorescence spectroscopy analysis, and the water sensitive papers were scanned using an image processing software to obtain droplet deposition coverage rate. The results showed that distribution curves of the coverage rate obtained by the paper tape method coupled with the fluorescence spectrum tracer were consistent with that obtained from the images of the water sensitive paper method, with the R² being 0.88~0.96. Because not all fine droplets fell on the water sensitive papers due to the effect of the high speed rotating rotor, the coverage rate curve measured by the continuous fluorescence method had multiple peaks and the value of its coverage rate was higher than that measured from the water sensitive paper method. When the unmanned helicopter flew at speed of 2 m/s and height of 3 m, the coverage ratio obtained from the continuous fluorescence method was up 16.92% compared to that sampled from the individual water-sensitive paper method, while when the flight speed was 4 m/s at height of 9 m, the coverage ratio in the latter was 97.77% higher than in the former. In terms of the impacts of unmanned helicopter operating conditions on coverage rate, when the helicopter flew at 2 m/s and height of 3 m, the coverage rate of the droplets obtained from the two methods were the highest, being 8.34% for the continuous fluorescence method and 7.14% for the individual paper method. With the flight height and speed increasing, the spatial coverage rate of the droplets decreased. In summary, the high-speed rotor of UAV generates a downwash, making the droplets of pesticides move in different directions and resulting in a large spatial difference in their deposition on the ground. Therefore, the continuous sampling method is more adequate to evaluate the spatial distribution of the droplets. This study has implication for study on detecting deposition of pesticides and other agrochemicals applied by UAV. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to achieve the purpose of reducing pesticide application while increasing efficacy, adjuvant has become an essential part of pesticide application process. The application amount of adjuvant directly affects the pesticide utilization. In order to study the effects of adjuvant on volume median diameter (VMD) of spray droplet and relative span of droplet size, a laser diffraction system was used to investigate the spray droplet characteristics of different adjuvants with different hydraulic nozzles. Three different adjuvants were tested with 2 types of nozzles, IDK120-025 and LU120-015, under laboratory conditions. The 3 adjuvants used in the experiment were Italy, Jijian and Urea. Italy and Jijian are standard adjuvants. Urea is a kind of chemical nitrogen fertilizer, and when it is dissolved in water the liquid becomes cloudy. Generally it is applied to aerial spraying solution to increase droplet settlement. As Urea does not belong to the standard adjuvants, its effect on the atomization of the liquid is not yet clear, and solving this problem is also one of the experimental purposes of this experiment. Italy and Urea are powdery and Jijian is liquid form. IDK nozzle and LU nozzle are widely used in the spraying, and the nozzle size and spray angle are the same, but the spay chamber atomization structure is different. The internal structure design of the IDK nozzle utilizes the Venturi principle to mix the spray liquid with the air inside the nozzle to make the ejected droplets become small bubbles. Studies have shown that changes in the physical and chemical properties of the spray liquid system play a decisive role in the correlation between the liquid film length and droplet size. Because the difference in physical and chemical properties of tap water and distilled water may affect the particle size of droplets, the experiment also uses tap water and distilled water as the experimental control groups. The results show that the 3 adjuvants have a more significant effect on IDK120-025. However, the spray mist droplet uniformity of LU120-015 nozzle is better than IDK120-025 nozzle. When the concentration ratio of Jijian solution is 1:3000, the VMD of droplets sprayed by IDK nozzle can be increased by 20.43% compared to water at a pressure of 0.4 MPa, and the relative span of droplet size can be reduced by 1.74%. When the concentration ratio of Italy solution is 1:2000, the VMD of droplets sprayed by IDK nozzle can be increased by 11.10% compared to water at a pressure of 0.4 MPa, and the relative span of droplet size can be reduced by 8.86%. When the concentration ratio of Italy solution is 1:3000, the VMD of droplets sprayed by LU nozzle can be reduced by 5.99% compared to water at a pressure of 0.2 MPa, and the relative span of droplets size can be increased by 1.56%. When the concentration ratio of Urea solution is 1:2 000, the VMD of droplets sprayed by IDK nozzle can be increased by 16.92% compared to water at a pressure of 0.4 MPa, and the relative span of droplet size can be reduced by 6.92%. According to the ASABE S572.1 standard to evaluate the result of the experiment, the spray quality of IDK nozzle is focused on medium and coarse grades, the VMD of droplets is large and the difficulty of retention on wet leave surface is moderate, and the drift potential is at medium level. The spray quality of LU nozzle is focused on fine grade, the VMD of droplets is small and the retention ability on wet leave surface is good, but the drift potential is high. This experiment can provide foundation for the selection of adjuvants and nozzles in field pesticide application, and provide the data basis for further research on new nozzles and adjuvants. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the traditional Fourier transform method, the problem of the harmonic components of the power grid cannot be analyzed in real time because of the lack of time resolution and the weak ability to resist noise. The powerful time-frequency method of wavelet transform, wavelet packet transform and short-time Fourier transform analysis is exploited to map the power harmonic with noise and time varying to the three-dimensional space of "time frequency amplitude". And studies are conducted from the aspects of real-time, time resolution, frequency resolution and noise immunity to explore the best method to analyze harmonic analysis in power system. The research shows that the advantage of short time Fourier transform in analyzing harmonic of power system can be an effective method to analyze harmonic in power system. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)