Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

Application of MR Technology in Teaching:A Case Study of UAV Agriculture&Forestry Plant Protection Curriculum

In order to solve the problems of insufficient training equipment,relatively lack of curriculum resources and single teaching means in the teaching of UAV(unmanned aerial vehicle)applied technology major,this paper studies the application of MR(Mixed Reality)in UAV applied technology major teaching,with the teaching of UAV agriculture&forestry plant protection curriculum as the carrier.The study will solve the pain points in teaching,improve the teaching ability and teaching information level,and increase the talent training quality of UAV,agriculture&forestry plant protection and related majors.Furthermore,it will create a protective,interactive,remote and scalable teaching experience for stu-dents,which can improve the teaching effect and reduce the teaching cost.

The role of glacial gravel in community development of vascular plants on the glacier forelands of the Third Pole

On a deglaciated terrain,glacial gravel is the primary component of the natural habitat for vascular plant colonization and succession.Knowledge regarding the role of glacial gravel in vascular plant growth,however,remains limited.In this study,an unmanned aerial vehicle(UAV)was used to investigate plant family composition,species richness,fractional vegetation cover(FVC),and gravel cover(GC)along elevational gradients on the three glacier forelands(Kekesayi,Jiangmanjiaer,and Koxkar Baxi)of the Third Pole(including the eastern Pamir Plateau and western Tianshan Mountains)in China.We then analyzed the spatial characteristics of vascular plants followed by exploring the effect of glacial gravel on vascular plants.Findings indicated that FVC on these glacier forelands generally decreased as the elevation increased or distance from the current glacier terminus decreased.The shady slope(Kekesayi)was more vegetated in comparison to the sunny slope(Jiangmanjiaer)at the glacier basin scale,and the warm and humid deglaciated terrain(Koxkar Baxi)had the highest FVC at the regional scale.Plant family composition and species richness on the glacier forelands decreased with rising elevation,with the exception of those on the Jiangmanjiaer glacier foreland.The relationships between FVC and GC presented negative correlations;particularly,they exhibited variations in power functions on the Kekesayi and Jiangmanjiaer glacier forelands of the eastern Pamir Plateau and a linear function on the Koxkar Baxi glacier foreland of the western Tianshan Mountains.Glacial gravel was found to be conducive to vegetation colonization and development in the early succession stage up until vascular plants adapted to the cold and arid climatic condition,whereas it is unfavorable to the expansion of vascular plants in the later succession stage.These findings suggested that the spatial difference of plant characteristics had close connections with regional climatic and topographic conditions,as well as glacial gravel distribution.In addition,we concluded that aerial photographs can be an asset for studying the functions of micro-environment in vegetation colonization as well as succession on the glacier forelands.

Winter Wheat Crop Height Estimation Using Small Unmanned Aerial System (sUAS)

Deploying the small Unmanned Aerial System (sUAS) for data collection of high-resolution images is a big potential in determining crop physiological parameters. The advantage of using sUAS technology is the ability to acquire a high-resolution orthophoto and a 3D Model which is highly suitable for plant height monitoring. Plant height estimation has a big impact in the growth and development of wheat because it is essential for obtaining biomass, which is a factor for higher crop yield. Plant height is an indicator of high yield estimation and it correlates to biomass, nitrogen content, and other plant growth parameters. The study is aimed to determine an accurate height of wheat using the sUAS generated Digital Surface Model (DSM). A high-resolution imagery between 1.0 - 1.2 cm/pixel was obtained from a 35 m altitude with area coverage of 1.01 hectares. The DSM and orthophoto were generated from the sUAS, and the computed wheat heights were derived from the difference of Digital Elevation Model (DEM) and DSM data. Field measurement using steel tape was done for ground truth. The sUAS-based wheat height data were evaluated using the ground truth of 66 wheat-rows by applying correlation and linear regression analysis. Datasets were collected from three different flight campaigns (March 2018-May 2018). The sUAS-based wheat height data were significantly correlated, obtaining the result of R2 = 0.988, R2 = 0.996 and R2 = 0.944 for the month of March, April and May 2018 respectively. The significance of linear regression results was also validated by computing for the p-value. The p-value results were 0.00064, 0.0000824 and 0.0058 respectively. The main concern is the lodging of winter wheat, especially during the month of April which affects the recording of the plant’s height. Because some of the wheat plants are now lying on the ground, so measurements are done vertically. Nonetheless, the results showed that sUAS technology is highly suitable for many agricultural applications.

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.

Design and test of a six-rotor unmanned aerial vehicle(UAV)electrostatic spraying system for crop protection

In recent years,multi-rotor unmanned aerial vehicle(UAV)crop protection operations have experienced tremendous growth.Compared with manual operations,they have advantages such as high operational efficiency,small pesticide dosage,and low pesticide hazards for humans.However,the tiny droplets produced during UAV spraying for crop protection are affected by the rotor air flow and will drift in all directions in an uncontrollable manner,severely affecting the pesticide deposition pattern and resulting in pesticide waste.To improve pesticide use efficiency during multi-rotor UAV spraying,an electrostatic spray system was designed based on electrostatic spray technology and a six-rotor UAV.The proper operation parameters for the UAV electrostatic spray were determined by test,which were spray altitude of 50 cm above the crop,spray pressure of 0.3 MPa and charging voltage of 9 kV.Field test was performed based on these parameters.The results showed that compared with non-electrostatic spray,the electrostatic spray improved by 13.6%in the average deposition density above the sampling device and 32.6%in the middle.The research can provide a reference for designing multi-rotor UAV electrostatic spray devices.

Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.

Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

单旋翼无人机植保作业系统的研制与实验研究

为了提高植保无人机的工作效率,降低无人机的操作难度,研制了单旋翼无人机植保作业系统。根据植保作业流程,单旋翼无人机植保作业系统实现启动、预热、起飞、飞行、喷洒作业、加药和返航等全过程无人工干预的自动飞行作业。单旋翼无人机植保控制系统包括作业平台、飞行控制、数据链路、地面站系统、系统应用、飞行器管理、用户信息管理、专业知识管理、大数据业务,实现了喷洒区域的精准控制,地面站系统与飞行系统的控制与交互,植保信息的存贮与分析等。通过实验研究表明:单旋翼无人机植保作业系统有效提高了植保工作效率,降低了飞行操作人员的操作难度。

中国民用无人驾驶航空器监管立法之完善

随着民用无人驾驶航空器技术的迅猛发展及其广泛应用,现有法律法规已难以全面覆盖和有效管理这一新兴领域。通过对当前民用无人驾驶航空器监管的立法现状进行梳理,分析当前民用无人驾驶航空器监管立法中存在的主要问题,在此基础上,提出具体的监管立法完善建议,如加强立法规划、制定专门规制民用无人驾驶航空器的法律、强化技术标准、明确各方责任、增加隐私权保护和数据安全方面的立法。通过这些措施,旨在构建一个全面、科学的民用无人驾驶航空器监管法律体系,为民用无人驾驶航空器产业的健康有序发展提供坚实的法律保障。

无人机用气力式喷粉装置设计及作业效果分析

林业病虫害是危害森林健康及其生态系统作用的主要原因之一。目前,我国针对橡胶树这类高大乔木的施药方式多为传统的地面喷雾和喷粉机械,该类作业方式受地形限制、人力需求大、劳动效率低,且难以对高处冠层施药。当前用于无人机挂载的喷粉装置多为地面喷粉器械的简单搭载,这易造成粉剂堵塞,喷施均匀性难以保证。为改善当前喷粉作业的效果,本研究设计了一种搭载在无人机上的气力式喷粉装置,利用高速气流将粉剂沿管道吹出,并对关键部件进行模拟仿真。结果表明,对于本研究设计的气力喷粉管道,涵道风机风速达10 m/s可满足绝大多数粉剂的输送要求。以飞扬倾角和飞扬高度为评价指标,模拟了不同安装位置下无人机喷粉作业的效果,模拟分析表明,为更好地抑制粉剂飞扬,气力喷粉装置的喷粉出口应正处旋翼下方,并后置安装在无人机上,其飞扬倾角为84°,飞扬高度为0.6 m。为探究试验样机的作业参数对喷粉沉积覆盖率、均匀性与穿透性的影响,开展了喷粉流量、作业高度与飞行速度3因素3水平的正交试验,结果表明,沉积覆盖率最佳作业参数为:喷粉流量6.0 kg/min、作业高度2 m、飞行速度2 m/s。沉积均匀性最佳作业参数为:喷粉流量3.6 kg/min、作业高度2.5 m、飞行速度6 m/s。沉积穿透性最佳作业参数为:喷粉流量6.0 kg/min、作业高度2.5 m、飞行速度4 m/s。为获得最优的试验方案,采用模糊综合评分法进一步分析,结果表明,最佳作业参数为喷粉流量6.0 kg/min、作业高度2.5 m、飞行速度2 m/s,相应的喷粉覆盖率均值为14.06%,喷粉均匀性均值为34.81%,喷粉穿透性为5.61%。因此,本研究的气力式喷粉无人机进行植保作业时,需结合实际作业对象,对沉积覆盖率、均匀性、穿透性不同需求综合分析,合理选择无人机喷粉的作业参数。研究结果可为高大乔木病虫害关键防治设备的研发提供新思路,对实现林业精准病虫害防治具有重要价值。

城市复杂环境下多目标无人机路径规划研究

针对当前无人机(Unmanned aerial vehicle,UAV)在城市范围内运行的噪声、成本与安全问题,对空域进行高度层划分,并在空气动力学等约束条件下,提出了基于噪声保护区的运行成本与坠地风险模型,满足UAV标准运行条件的同时,不仅降低其对环境和地面人群的噪声影响,还降低其运行成本与坠地风险。在此基础上,基于噪声保护区,运用改进Dubins路径规划方法,将Dubins路径规划思想与几何圆的切线相结合,增设节点处理,丰富UAV运行可选路径,并对路径进行优化处理。以Dijkstra算法为搜索最佳路径算法、总成本最低为目标,搜索一条UAV最佳运行路径,并与A*算法进行研究比对。仿真实验验证了所提模型和改进方法的有效性,降低了UAV运行噪声影响,缩减了运行成本,提高了运行的安全性和效率。据算例得出结果:双旋翼中型UAV(质量约15 kg,桨盘面积约1.313 m^(2))最佳运行高度为40 m,其运行最低总成本为5.42,较其余高度层运行总成本最高减少37.56%,最低减少5.91%。

植保无人机喷施封闭除草剂对稻田杂草的防效研究

为探究植保无人机喷施除草剂对直播稻田杂草防效,采用50%丁草胺乳油、26%噁草酮悬浮剂、350 g/L异噁草松微囊悬浮剂、300 g/L丙草胺乳油4种封闭除草剂,在飞行高度2 m和3 m,用水量22.5、45.0 L/hm^(2)条件下设置不同处理,考察播前施药结合水层管理及播后干封对水稻安全性及对直播稻田不同种类杂草的防除效果。结果表明,26%噁草酮悬浮剂2个用水量处理的成苗率和苗高显著低于其余处理,播后30 d各处理水稻分蘖数均显著高于空白对照,各处理5株苗茎基宽与空白对照无差异。药后15 d,3种除草剂供试浓度下高用水量处理对稗草、千金子、多花水苋、异型莎草等杂草的防效均优于低用水量;播前水封与播后干封效果整体上差异不大。药后40 d,整体上各处理防效均降低,26%噁草酮悬浮剂、350 g/L异噁草松微囊悬浮剂在高用水量时对稗草的株数防效显著优于低用水量,3种药剂在高用水量时对千金子的株数防效均显著高于低用水量,鲜重防效与株数防效趋势基本一致。3种药剂不同飞防参数下对异型莎草和多花水苋的防效有差异,且效果较差。同一药剂在用水量45 L/hm^(2)和飞行高度3 m时对不同杂草的防效优于用水量22.5 L/hm^(2)和飞行高度2 m处理。不同除草剂对不同种类杂草的防效存在差异,高用水量播前施药结合水层管理对不同杂草的防效均优于播后干封处理。后期应适时进行茎叶处理,以提升施药对田间杂草的治理效果。

无人机植保在水稻病虫害防治中的实践与研究

为探寻更科学的水稻植保方式,在水稻病虫害防治过程中合理施用农药,减少水稻农药残留超标,提高水稻的产量和质量,本研究团队在合作企业的协助下,以乡镇合作社或个人承包的稻田为试验样本,合理设计施药方式及参数,通过多组对照农田的试验,发现其中一组无人机植保的水稻,在较少的药剂使用情况下,取得了较好的病虫害防治效果。

提升水稻上植保无人机喷施药液沉积效果的关键因素

旨在解决植保无人机在水稻上施药后存在雾滴分布不均、农药沉积率不高等实际田间作业问题,比较分析了飞防助剂、施药液量、飞行速度对植保无人机在水稻上施药后雾滴分布和农药沉积率的影响,明确可产生显著性差异的关键因素,进一步分析该因素是否影响农药混配药液的微粒物理性质和润湿性能。结果表明:在三因素三水平的正交试验中,不同试验处理在上层收集的雾滴的面积显著大于中层和下层,加入助剂促使试验处理在不同高度上的雾滴分布较为均匀,它们在中、下层收集的雾滴的面积占比明显得到提高;飞防助剂、施药液量和飞行速度可不同程度地影响农药沉积率,农药沉积率范围在43.72%~68.63%;体现3个因素对农药沉积率影响程度的R值分别为13.44、6.25和4.93,其中仅飞防助剂产生的差异达显著性水平;在药液混配后0~48 h内,加入2种不同助剂均可显著降低其表面张力和静态接触角,加入2种不同助剂的药液之间则未表现显著差异;此外,呈现较高农药沉积率的助剂品种则可同时有效降低药液在混配后0~48 h内的粒径大小和粒径分散程度。研究结果对于植保无人机在水稻上的精准施药技术提升具有重要参考依据,可以提高农药沉积率,减少农药流失。

生鲜农产品车辆和无人机组合配送路径优化及效果测试

【目的】为提升生鲜农产品的配送效率,提出生鲜农产品车辆与无人机组合配送路径优化方案。【方法】按照生鲜农产品冷链配送体系要求,采用模糊K-means聚类方法确定生鲜农产品配送中心;定义配送中心位置为车辆和无人机组合配送的起点,以生鲜农产品配送总成本最小为目标函数,利用蚁群算法和无人机物流车协调配送算法求解上述构建的目标函数,获取最低配送总成本的配送路径规划结果;引入安全启发函数优化蚂蚁转移规则,获取安全最高的无人机最优配送路径。再按照该方法,将生鲜农产品按照质量划分为重件和轻件,并计算重件和轻件在配送过程中产生的成本,获取在不同的总配送生鲜农产品快件数量下,优化前与优化后的配送成本。【结果】效果测试表明,该方法具有良好的聚类效果,簇类凝聚度(Davies-Bouldin, DB)指数值均低于0.15,可合理选取配送中心,保证配送中心的覆盖程度。并且通过对比该方法应用前后的数据对比可知,应用前重件和轻件的配送成本明显更高于应用后的成本。【结论】该方法可以更高质量地完成车辆和无人机组合配送路径规划,能够显著降低生鲜农产品的配送总体成本,应用效果满足应用需求,提升配送效率。