基于MEMS传感器的两轴姿态调整系统设计与试验

在精准农业生产过程中,传感器实时采集作物信息或环境状态,传感器与作物的相对位置,直接影响到采集数据的准确性,及后期处理的效率,甚至影响到作业的效果。而田间道路、垄间颠簸,会影响传感器与作物相对位置,造成信息失真和不准确,为了减少地面不平整干扰对传感器位置的影响,该文提出了基于MEMS传感器步进电机驱动的两轴姿态调整系统。该研究分析了系统的工作原理和控制方法,以陀螺仪、重力加速度计为姿态测量元件,步进电机为驱动部件,设计基于单片机控制的两轴姿态调整系统平台软硬件结构。系统采用单片机对陀螺仪和加速度计信息的实时采样,建立了多传感信息的融合算法和姿态判定模型,可以实时分析检测对象姿态,并输出控制步进电机,对平台姿态进行补偿调整,保持控制对象的相对惯性空间方位不变,实现了平台姿态平衡的快速控制。同时系统加入了绝对位置传感器,实现初始工作状态的自动复位。测试试验结果表明,系统运行稳定,单轴姿态调整精度在平整坡路状态下最大误差在0.5°以内,在田间颠簸路况运行下最大误差在3.0°以内,能够满足信息采集和检测过程中姿态自动调整、保持相对位置的控制要求。利用该控制系统,能够提高信息采集的准确性,在精准农业生产中具有应用作用。

无人机农业遥感在农作物病虫草害诊断应用研究进展

农田作物信息的快速获取与解析是开展精准农业实践的前提和基础。根据农作物病虫草害的实际程度进行变量喷施和作业管理,可减少农业生产成本、优化作物栽培、提高农作物产量和品质,从而实现农业精准管理。近年来,随着无人机产业的快速发展,无人机农业遥感技术因其空间分辨率高、时效性强和成本低等特点,在农作物病虫草害监测应用中发挥了重要作用。本文首先介绍了精准农业航空的基本思想与系统组成和无人机遥感在精准农业航空的地位。接着探讨了无人机农业遥感系统常见的成像方式和遥感影像解析方法,并阐述了国内外无人机农业遥感技术在农作物病虫草害检测研究的最新进展。最后总结了无人机农业遥感技术发展至今面临的挑战并展望了未来的发展方向。本文将为开展无人机农业遥感技术在精准农业航空领域的研究提供理论参考和技术支撑。

Using an Electronic Nose to Rapidly Assess Grandlure Content in Boll Weevil Pheromone Lures

Samples of pheromone lures used in boll weevil, Anthonomus grandis （Boheman）, eradication programs are routinely analyzed by Gas Chromatography （GC） to ensure lures are adequately dosed with grandlure, the synthetic aggregation phero- mone produced by male weevils. However, preparation of GC samples is tedious, time consuming, and requires a moderate level of experience. We examined the use of a commercially-available electronic nose （e-nose） for rapidly assessing the grandlure contents of lures. The e-nose was trained to recognize headspace collections of grandlure emitted from new lures and after lures were aged under field conditions for 4 d, 7 d, 10 d, and 14 d. Based on cross-validation of the training set, the e-nose was 82% accurate in discriminating among the different age classes of lures. Upon sampling headspace collections of pheromone from a different set of field-aged lures, the e-nose was 〈50% accurate in discriminating 4 d, 7 d, and 10 d aged lures from the other age classes of lures. However, the e-nose identified new and 14 d aged lure samples with 100% accuracy. In light of these findings, e-nose technology shows considerable promise as an alternative approach for rapidly assessing the initial grandlure contents of lures used in boll weevil eradication programs.

Novel real-time safety algorithm for predicting multi-targets in the farmland road

The more information obtained about the driving environment,the more ensures driving safety.Due to the complex driving environment of farmland roads,targets beside the road sometimes have an important impact on driving safety.To achieve this goal,a novel real-time detection and prediction algorithm of targets was proposed.The whole image was divided into four parts by RCM:driving region,crossroad region,roadside region,and the other region.In addition,a safety policy for every part was enforced by the algorithm,which was based mainly on the combination of the YOLACT and GPM.On this basis,a self-collected data set of 5000 test samples is used for testing.The detection accuracy of the algorithm in the data set could reach up to 90%,and the processing speed to 30.4 fps.In addition,experiments were carried out on actual farmland roads,and the results showed that the proposed algorithm was able to detect,track,and predict targets on the farmland road,and alarm to driver in time before the targets rush into the road.This study provides an important reference for the safe driving of agricultural vehicles.

Downwash airflow field distribution characteristics and their effect on the spray field distribution of the DJI T30 six-rotor plant protection UAV

Spray characteristics are the fundamental factors that affect droplet transportation downward,deposition,and drift.The downwash airflow field of the Unmanned Aviation Vehicle(UAV)primarily influences droplet deposition and drift by changing the spray characteristics.This study focused mainly on the effect of the downwash airflow field of the UAV and nozzle position on the droplet spatial distribution and velocity distribution,which are two factors of spray characteristics.To study the abovementioned characteristics,computational fluid dynamics based on the lattice Boltzmann method(LBM)was used to simulate the downwash airflow field of the DJI T30 six-rotor plant protection UAV at different rotor rotational speeds(1000-1800 r/min).A particle image velocimetry system(PIV)was utilized to record the spray field with the downwash airflow field at different rotational speeds of rotors(0-1800 r/min)or different nozzle positions(0,0.20 m,0.35 m,and 0.50 m from the motor).The simulation and experimental results showed that the rotor downwash airflow field exhibited the‘dispersion-shrinkage-redispersion’development rule.In the initial dispersion stage of rotor airflow,there were obvious high-vorticity and low-vorticity regions in the rotor downwash airflow field.Moreover,the low-vorticity region was primarily concentrated below the motor,and the high-vorticity region was mainly focused in the middle area of the rotors.Additionally,the Y-direction airflow velocity fluctuated at 0.4-1.2 m under the rotor.When the rotor airflow developed to 3.2 m below the rotor,the Y-direction airflow velocity showed a slight decrease.Above 3.2 m from the rotor,the Y-direction airflow velocity started to drastically decrease.Therefore,it is recommended that the DJI T30 plant protection UAV should not exceed 3.2 m in flight height during field spraying operations.The rotor downwash airflow field caused the nozzle atomization angle,droplet concentration,and spray field width to decrease while increasing the vortex scale in the spray field when the rotor system was activated.Moreover,the increase in rotor rotational speed promoted the abovementioned trend.When the nozzle was installed in various radial locations below the rotor,the droplet spatial distribution and velocity distribution were completely different.When the nozzle was installed directly below the motor,the droplet spatial distribution and velocity distribution were relatively symmetrical.When the nozzle was installed at 0.20 m and 0.35 m from the motor,the droplets clearly moved toward the right under the induction of stronger rotor vortices.This resulted in a higher droplet concentration in the right-half spray field.However,the droplet moved toward the left when the nozzle was installed in the rotor tip.For four nozzle positions,when the nozzle was installed at 0 or 0.20 m from the motor,the droplet average velocity was much higher.However,the droplet average velocity was slower when the nozzle was installed in the other two positions.Therefore,it is recommended that the nozzle is installed at 0 or 0.20 m from the motor.The research results could increase the understanding of the downwash airflow field distribution characteristics of the UAV and its influence on the droplet spatial distribution and velocity distribution characteristics.Meanwhile,the research results could provide some theoretical guidance for the choice of nozzle position below the rotor.

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 R^(2)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 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),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.

红蜘蛛叶螨感染的棉花植株的光谱响应:螨密度和杀螨剂浓度的研究

棉红蜘蛛(亚纲:叶螨科)是许多农业系统的重要害虫,现已经发现在玉米、棉花、高粱中造成过经济损失。成年红蜘蛛叶螨的生物测定指出,Temprano·(阿维菌素)对于成年叶螨来说是毒性最强的杀螨药。从航空应用的角度来看,还需要更多的研究来确定杀螨药的航空应用参数。本研究的目的是探索感染不同叶螨密度及用不同浓度杀螨药处理的棉花植株的光谱响应。结果显示,棉花植物感染的螨虫密度不同,光谱特征明显不同。通过分析五种不同Temprano处理(对照,1/8,1/4,1/2,1)的棉花植株后发现,光谱反射率曲线明显不同。550 nm、560 nm、680 nm和740 nm四种波长对检测不同Temprano处理的叶螨感染的棉花植株光谱差异非常重要。归一化植被指数图像(NDVI)能够检测出棉花植物损伤的级别。1/2 Temprano处理的样本和1 Temprano处理的样本效果一样。本研究的发现可能会有助于降低用于作物生产和保护的杀螨药的成本和数量。

Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions

Spray drift has always been a focus research area in the field of unmanned aerial vehicle(UAV)application.Under the fixed premises of UAV operating parameters,such as height,speed and spraying liquid,the droplet drift is mainly affected by meteorological conditions.In this research,the spray drift and deposition tests were conducted using a QuanFeng120 UAV in a pineapple field under various different meteorological conditions.The experimental results showed that with the changes of UAV operating height and wind speed,the start position of the in-swath deposition area changed 4 m in the extreme situation.The percentage of the total spray drift was from 15.42%to 55.76%.The position of cumulative spray drift that accounted for 90%of the total spray drift was from 3.70 m to 46.50 m relative to the flight line.According to the downwind spray drift curve,the nonlinear equations of the same type under the four operating conditions of the UAV were fitted.The spray drift and the deposition of UAV application were significantly affected by different meteorological conditions and UAV operating heights.The results could provide a theoretical basis for UAV spraying in pineapple plants and support for spray drift control and prediction.

Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle(UAV)

With changing climate and farmland ecological conditions,pest outbreaks in agricultural landscapes are becoming more frequent,increasing the need for improved crop production tools and methods.UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products.This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection,and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals.Additionally,the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed,and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing,variable-rate technologies,and spray drift models are considered.Based on the reviewed literature,future developments and the impacts of these UAV technologies are projected.This review may inspire the innovation of the combined use of big data analytics and UAV technology,precision agricultural spraying technology,drift reduction technology,swarm UAV cooperative technology,and other supporting technologies for UAV-based aerial spraying for scientific research in the world.

Classification method of cultivated land based on UAV visible light remote sensing

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.

Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model

In this study,an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit(SWD)for maize and sunflower grown under full and deficit irrigation treatments.The proposed model was applied to optimize the maximum total available soil water(TAWr)by minimizing the difference between a water stress coefficient ks and crop water stress index(1-CWSI).The optimal value of maximum TAWr was then used to calibrate a soil water balance model which in turn updated the estimation of soil water deficit.The estimates of SWD in the soil profile of both irrigated maize and sunflower fields were evaluated with the crop root zone SWD derived from neutron probe measurements and the FAO-56 SWD procedure.The results indicated a good agreement between the estimated SWD from the proposed approach and measured SWD for both maize and sunflower.The statistical analyses indicated that the maximum TAWr estimated from CWSI significantly improved the estimates of SWD,which reduced the mean absolute error(MAE)and root mean square error(RMSE)by 40%and 44%for maize and 22%for sunflower,compared with the FAO-56 model.The proposed procedure works better for crops under deficit irrigation condition.With the availability of higher spatial and temporal resolution airborne imagery during the growing season,the optimization procedure can be further improved.

Detection of the foreign object positions in agricultural soils using Mask-RCNN

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.

Review of deep learning-based weed identification in crop fields

Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL.Through an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification technology.Emphasis is placed on data sources and DL models applied to different technical tasks.Additionally,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed identification.Corresponding solutions are proposed to provide a reference for future research directions in weed identification.

GEE-Based monitoring method of key management nodes in cotton production

The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital forfield cotton macro-statistics,particularly for predicting cotton production and obtaining comprehensive data.This study examines Shihezi,Xinjiang as a case study,utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021.Three machine learning models(RF,SVM,and CART)were employed to extract annual crop classification area rasters,monitor weekly cultivation progress,and monitor abandoned cropland during the cultivation period.The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas,achieving the producer’s accuracy of the cotton category reached 98.5%,and the kappa coefficient is 0.947.Cotton cultivated in 2021 began is a week later than in 2020,yet exhibited a faster cultivate speed.The proportion of abandoned cottonfields in the study area rose in 2020 compared to 2019.The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle.

Summer maize LAI retrieval based on multi-source remote sensing data

Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.

Effects of spray adjuvants and operation modes on droplet deposition and elm aphid control using an unmanned aerial vehicle

A conventional spraying mode and a fully autonomous fruit tree operation mode using a model DJ T30 unmanned aerial vehicle(UAV)were used to control aphids control on elm trees and to clarify the distribution of droplets in elm trees sprayed by a UAV.The effects of six aviation spray adjuvants on elm canopy droplet deposition and aphid control were evaluated.ImageJ software was used to analyze and measure the droplet density and deposition of water sensitive paper in two modes;this was done to calculate the droplet uniformity,depositional penetration,and droplet penetration,and to verify the aphid control effect.The results showed that the droplet density increased by 79.7%-100.7% in the upper canopy and 0-394.1%in the lower canopy without adjuvants in the fully autonomous fruit tree operation mode.The upper canopy deposits increased by 65.7%-179.3%,and the lower canopy increased by 0-152.8%.When adjuvants were added,the droplet density in the upper canopy increased by 49.7-56.1%using Jiexiaofeng(JXF),and the lower canopy increased by 138.2%-177.8% using JXF,45.8%-141.3%using Beidatong(BDT),45.5%-92.9% using Gongbei(GB),0-93.5% using Maisi(MS),and 0-95.2%using Manniu(MN).The deposits of the upper canopy increased by 888.1-1154.2% using JXF,0-1298.3% using MN,0-343.9%using BDT,0-422.5% using GB,0-580.3% using MS.The lower canopy increased by 746.4%-1426.0%using JXF,226.2%-231.0% using BDT,435.8%-644.0% using GB,255.0%-322.4%using MS,and 249.3%-360.0%using MN.When JXF was added,the droplet uniformity,droplet penetration and depositional penetration were better than when using other adjuvants.The effects of JXF,BDT and GB in controlling aphids was significantly better than other adjuvants(p<0.05).The following control effects were observed;94.1% with JXF,93.1% with BDT,and 93.3% with GB after 3 d of application,and 97.9% with JXF,95.6% with BDT,and 97.1% with GB after 7 d of application.At the same time,the application of the fully autonomous fruit tree operation mode and JXF can effectively improve the density and deposits,which will produce a superposition optimization effect.Our study focuses on the prevention and control of elm aphid infestations based on the operation mode of a UAV and aviation spray adjuvants,which can provide a baseline for the control of diseases and insect pests using UAVs in agriculture and forestry.

Drift and deposition of ultra-low altitude and low volume application in paddy field

Field trials were performed to evaluate various techniques for measuring spray deposition and aerial drift during spray application to paddy field.The application of a spraying agent containing the fluorescent dye Rhodamine-B was applied by an unmanned aerial vehicle(UAV)which flew at a height of 5 m,a speed of 3 m/s,and the wind speed of 3 m/s.The results showed that because the downdraft produced by a helicopter rotor increased the penetrability of crops,there is a higher deposition on the upper layer and the under layer than the traditional spraying.The average deposition on the upper layer accounts for 28% of the total spraying,the deposition on the under layer accounts for 26% of the total spraying.The deposition on the under layer takes up 92.8% of the deposition on the upper layer.Droplets drift data showed that the drift of non-target area took up 12.9% of the total liquid spray.The 90% drifting droplets were located within a range of 8 m of the target area;the drift quantity was almost zero at a distance of 50 m away from the treated area.

Current status and future trends of precision agricultural aviation technologies

Modern technologies and information tools can be used to maximize agricultural aviation productivity allowing for precision application of agrochemical products.This paper reviews and summarizes the state-of-the-art in precision agricultural aviation technology highlighting remote sensing,aerial spraying and ground verification technologies.Further,the authors forecast the future of precision agricultural aviation technology with key development directions in precision agricultural aviation technologies,such as real-time image processing,variable-rate spraying,multi-sensor data fusion and RTK differential positioning,and other supporting technologies for UAV-based aerial spraying.This review is expected to provide references for peers by summarizing the history and achievements,and encourage further development of precision agricultural aviation technologies.

Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management.Compared with piloted aircraft,an Unmanned Aerial Vehicle(UAV)can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision.They can also“fill in the gap”in locations where fixed winged or rotary winged aircraft are not readily available.In agriculture,UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials.Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management.Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well.This paper presents an overview of research involving the development of UAV technology for agricultural production management.Technologies,systems and methods are examined and studied.The limitations of current UAVs for agricultural production management are discussed,as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.

Effect of wind field below unmanned helicopter on droplet deposition distribution of aerial spraying

Wind field is one of the important factors affecting the distribution characteristics of aerial spraying droplet deposition.In order to reveal the impact mechanism of droplet deposition distribution by the wind field below agricultural unmanned helicopter rotor,in this study,the wind field distribution below uniaxial single-rotor electric unmanned helicopter rotor was measured by using a wireless wind speed sensor network measurement system for unmanned helicopter.The effects of wind field in three directions(X,Y,Z)below the rotor on droplet deposition distribution were analyzed with the condition of aerial spraying droplet deposition in rice canopy,and the regression model was established via variance and regression analyses of experiment results.The results showed that,the wind field in Y direction had a significant effect on droplet deposition in effective spray area,the wind field in Z direction had an extremely significant effect on droplet deposition in effective spray area,and the corresponding significance(sig.)values were 0.011 and 0.000.Furthermore,the wind field in Z direction had a significant effect on the penetrability and uniformity of droplet deposition in effective spray area,the corresponding sig.values were 0.025 and 0.011 respectively.The wind speed in Y direction at the edge of effective spray area had a significant effect on droplet drift,the sig.value was 0.021.In addition,the correlation coefficient R of the regression model was 0.869 between droplet deposition in effective spray area and the wind speed in Y and Z directions,and 0.915 between the uniformity of droplet deposition in effective spray area and the maximum wind speed in Z direction.The result revealed the influencing mechanism of the wind field below the rotor of uniaxial single-rotor electric unmanned helicopter on the distribution of aerial spraying droplet deposition.The results can provide guidance for the actual production application of aerial spraying to reduce liquid drift and improve the utilization rate of pesticide.