Unveiling the spatial distribution and molecular mechanisms of terpenoid biosynthesis in Salvia miltiorrhiza and S. grandifolia using multi-omicsand DESI-MSI

Salvia miltiorrhiza and S.grandifolia are rich in diterpenoids and have therapeutic effects on cardiovascular diseases.In this study,the spatial distribution of diterpenoids in both species was analyzed by a combination of metabolomics and mass spectrometry imaging techniques.The results indicated that diterpenoids in S.miltiorrhiza were mainly abietane-type norditerpenoid quinones with a furan or dihydrofuran D-ring and were mainly distributed in the periderm of the roots,e.g.cryptotanshinone and tanshinone IIA.The compounds in S.grandifolia were mainly phenolic abietane-type tricyclic diterpenoids with six-or seven-membered C-rings,and were widely distributed in the periderm,phloem,and xylem of the roots,e.g.11-hydroxy-sugiol,11,20-dihydroxy-sugiol,and 11,20-dihydroxy-ferruginol.In addition,the leaves of S.grandifolia were rich in tanshinone biosynthesis precursors,such as 11-hydroxy-sugiol,while those of S.miltiorrhiza were rich in phenolic acids.Genes in the upstream pathway of tanshinone biosynthesis were highly expressed in the root of S.grandifolia,and genes in the downstream pathway were highly expressed in the root of S.miltiorrhiza.Here,we describe the specific tissue distributions and mechanisms of diterpenoids in two Salvia species,which will facilitate further investigations of the biosynthesis of diterpenoids in plant synthetic biology.

Development and Evaluation of an Optical Sensing System for Detection of Herbicide Spray Droplets

Real time monitoring of herbicide spray droplet drift is important for crop production management and environmental protection. Existing spray droplet drift detection methods, such as water-sensitive paper and tracers of fluorescence and Rubidium chloride, are time-consuming and laborious, and the accuracies are not high in general. Also, the tracer methods indirectly quantify the spray deposition from the concentration of the tracer and may change the drift characteristics of the sprayed herbicides. In this study, a new optical sensor system was developed to directly detect the spray droplets without the need to add any tracer in the spray liquid. The system was prototyped using a single broadband programmable LED light source and a near infrared sensor containing 6 broadband spectral detectors at 610, 680, 730, 760, 810, and 860 nm to build a detection system for monitoring and analysis of herbicide spray droplet drift. A rotatory structure driven by a stepper motor in the system was created to shift the droplet capture line going under the optical sensor to measure and collect the spectral signals that reflect spray drift droplets along the line. The system prototype was tested for detection of small (Very Fine and Fine), medium (Medium), and large (Coarse) droplets within the droplet classifications of the American Society of Agricultural and Biological Engineers. Laboratory testing results indicated that the system could detect the droplets of different sizes and determine the droplet positions on the droplet capture line with 100% accuracy at the wavelength of 610 nm selected from the 6 bands to detect the droplets.

Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass

Glyphosate is widely used in row crop weed control programs of glyphosate-resistant (GR) crops. With the accumulation of glyphosate use, several weeds have evolved resistance to glyphosate. In order to control GR weeds for profitable crop production, it is critical to first identify them in crop fields. Conventional method for identifying GR weeds is destructive, tedious and labor-intensive. This study developed hyperspectral imaging for rapid sensing of Italian ryegrass (Lolium perenne ssp. multiflorum) plants to determine if each plant is GR or glyphosate-susceptible (GS). In image analysis, a set of sensitive spectral bands was determined using a forward selection algorithm by optimizing the area under the receiver operating characteristic between GR and GS plants. Then, the dimensionality of selected bands was reduced using linear discriminant analysis. At the end the maximum likelihood classification was conducted for plant sample differentiation of GR Italian ryegrass from GS ones. The results indicated that the overall classification accuracy is between 75% and 80%. Although the accuracy is lower than the classification of Palmer amaranth (Amaranthus palmeri S. Wats.) in our previous study, this study provides a rapid, non-destructive approach to differentiate between GR and GS Italian ryegrass for improved site-specific weed management.

Mobile Open-Source Plant-Canopy Monitoring System

Many agricultural applications, including improved crop production, precision agriculture, and phenotyping, rely on detailed field and crop information to detect and react to spatial variabilities. Mobile farm vehicles, such as tractors and sprayers, have the potential to operate as mobile sensing platforms, enabling the collection of large amounts of data while working. Open-source hardware and software components were integrated to develop a mobile plant-canopy sensing and monitoring system. The microcontroller-based system, which incorporated a Bluetooth radio, GPS receiver, infrared temperature and ultrasonic distance sensors, micro SD card storage, and voltage regulation components, was developed at a cost of US$292. The system was installed on an agricultural vehicle and tested in a soybean field. The monitoring system demonstrates an application of open-source hardware to agricultural research and provides a framework for similar or additional sensing 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.

Characterization of spray deposition and drift from a low drift nozzle for aerial application at different application altitudes

A complex interaction of controllable and uncontrollable factors is involved in aerial application of crop production and protection materials.Although it is difficult to completely characterize spray deposition and drift,these important factors can be estimated with appropriate sampling protocol and analysis.Application height is an important variable influencing off-target spray drift,but this variable has not been easily measured or logged.A custom-configured aircraft-mounted laser with logging capabilities makes this possible.This study was designed to investigate droplet size and deposition characteristics of a low drift CP flat-fan nozzle at application altitudes 3.7 m,4.9 m,and 6.1 m.In the study,CP flat-fan nozzles were set to a downward angle of 30 degrees applying a mixture of water,Syl-Tac®adjuvant,and Rubidium Chloride(RbCl)tracer at a 28.5 L/ha application rate.Spray droplets were collected using water sensitive paper(WSP)cards placed in the spray swath.Mylar sheets were also placed in the swath and downwind for drift sampling.Statistical analysis indicated that median droplet diameter as determined by WSP in the spray swath was not significantly influenced by spray application height.Similarly,statistical analysis also indicated that concentration of RbCl tracer from Mylar samplers in the spray swath was not significantly influenced by application height.Application height had a significant effect on spray deposition from drift samplers,along with wind direction and relative humidity.Final results for drift samplers may have been influenced by shifts in wind direction that altered the relationship between orientation of samplers and wind.

Multispectral imaging systems for airborne remote sensing to support agricultural production management

This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production.The three systems have been used in agricultural studies.They range from low-cost to relatively high-cost,manually operated to automated,multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras.Practical issues regarding use of the imaging systems were described and discussed.The low-cost system,due to band saturation,slow imaging speed and poor image quality,is more preferable to slower moving platforms that can fly close to the ground,such as unmanned autonomous helicopters,but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft.With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount.The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering.This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated.The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated.

Characterizing downwind drift deposition of aerially applied glyphosate using RbCl as tracer

Rubidium chloride(RbCl)was used as a tracer tank-mixed with active ingredients to profile downwind deposition of aerially applied crop protection and production materials to characterize off-target drift,which helps improve spray efficiency and reduce environmental contamination.Mylar sheets were placed on a holder in the field at each sampling station to collect sprayed solution.RbCl tracer was used to assess downwind drift of nozzles mounted on the booms installed and controlled on both sides of an agricultural airplane.The experiment was conducted on a field covered by Bermuda grass(Cynodon dactylon).During the experiment,the airplane was planned to fly three passes with three replications at each of three different altitudes,3.7 m,4.9 m,and 6.1 m for total of 27 flight runs.The results indicated that sampling station location had a significant effect on RbCl concentration.However,application release altitude was not significant to the change of RbCl.Another practical application in the same aerial application system was used to assess crop injury from the off-target drift of aerially applied glyphosate.RbCl concentrations measured from Mylar sheets were correlated with visual injury,plant height,shoot dry weight,leaf chlorophyll content,and shikimate,which were measured from the leaves and plant samples collected.Overall,RbCl is an effective tracer for monitoring spray applications from agricultural aircraft and unmanned aerial vehicles to intensify agriculture output and minimize environmental impact.

Development and evaluation of low-altitude remote sensing systems for crop production management

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems(GIS),global positioning systems(GPS)and remote sensing,and is used to monitor soil,crop growth,weed infestation,insects,diseases,and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making.With the development of variable-rate technology,precision agriculture focuses more on tactical actions in controlling variable-rate seeding,fertilizer and pesticide application,and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season.With the development of aerial variable-rate systems,low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations.Airborne systems for multispectral imaging using a number of imaging sensors(cameras)were developed.Unmanned aerial vehicles(UAVs)provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes,and they are efficient and more flexible than manned agricultural airplanes,which often cannot provide images at both low altitude and low speed for capture of high-quality images.UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions.This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors.UAVs,as a special platform,were discussed for crop sensing based on the researchers'studies.

Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery

Cotton yield varies spatially within a field.The variability can be caused by various production inputs such as soil properties,water management,and fertilizer application.Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion.A 10-ha cotton field with irrigation and non-irrigation 2×2 blocks was used in this study.Six nitrogen application treatments were randomized with two replications within each block.As plant canopy was closed,airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera.The images were processed to generate various vegetation indices.The vegetation indices were evaluated for the best performance to characterize yield.The effect of irrigation on vegetation indices was significant.Models for yield estimation were developed and verified by comparing the estimated and actual yields.Results indicated that ratio of vegetation index(RVI)had a close relationship with yield(R^(2)=0.47).Better yield estimation could be obtained using a model with RVI and soil electrical conductivity(EC)measurements of the field as explanatory variables(R^(2)=0.53).This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation and considering soil properties and nitrogen.

Portable device to assess dynamic accuracy of global positioning system(GPS)receivers used in agricultural aircraft

A device was designed to test the dynamic accuracy of Global Positioning System(GPS)receivers used in agricultural aircraft and other aerial vehicles.The system works by directing a sun-reflected light beam from the ground to the aircraft using mirrors.A photo detector points downward from the aircraft to detect the light beam,and photo detection circuitry triggers an event in the guidance system data file at the aircraft’s location corresponding to the precisely georeferenced position on the ground.Construction details are presented on the mirror-based light reflection system and photo-electronic circuitry designed to trigger an event in the guidance system’s log file.An example application evaluated the horizontal accuracy of a stand-alone GPS receiver by matching dynamic data with data from the aircraft’s guidance system.Results indicated a 2.16 s lead in position registered by the stand-alone receiver over that registered by the aircraft’s guidance system GPS receiver,which had been previously evaluated to be within 0.13 s of Real-Time Kinematic(RTK)-referenced time and position.

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

Agricultural drought threatens food security.Numerous remote-sensing drought indices have been developed,but their different principles,assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas.Here,we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces,northern China(predominantly rain-fed and irrigated agriculture,respectively):vegetation health index(VHI),temperature vegetation dryness index(TVDI)and drought severity index(DSI).We compared the agreement between these indices and the standardized precipitation index(SPI),soil moisture,winter wheat yield and National Meteorological Drought Monitoring(NMDM)maps.On average,DSI outperformed the other indices,with stronger correlations with SPI and soil moisture.DSI also corresponded better with soil moisture and NMDM maps.The jointing and grain-filling stages of winter wheat are more sensitive to water stress,indicating that winter wheat required more water during these stages.Moreover,the correlations between the drought indices and SPI,soil moisture,and winter wheat yield were generally stronger in Shaanxi province than in Henan province,suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.

Ground-based hyperspectral remote sensing for weed management in crop production

Agricultural remote sensing has been developed and applied in monitoring soil,crop growth,weed infestation,insects,diseases and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture has been implemented through prescription mapping of crop fields at different scales with the data remotely sensed from space-borne,airborne and ground-based platforms.Ground-based remote sensing techniques offer portability,flexibility and controllability in applications for precision agriculture.In weed management,crop injury from off-target herbicide spray drift and herbicide resistance in weeds are two important issues.For precision weed management,ground-based hyperspectral remote sensing techniques were developed for detection of crop injury from dicamba and differentiation between glyphosate resistant and sensitive weeds.This research presents the techniques for ground-based hyperspectral remote sensing for these two applications.Results illustrate the advantages of ground-based hyperspectral remote sensing for precision weed management.

Identification of seedling cabbages and weeds using hyperspectral imaging

Target detection is one of research focuses for precision chemical application.This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging.In processing the image data with ENVI software,after dimension reduction,noise reduction,de-correlation for high-dimensional data,and selection of the region of interest,the SAM(Spectral Angle Mapping)model was built for automatic identification of cabbages and weeds.With the HSI(Hyper Spectral Imaging)Analyzer,the training pixels were used to calculate the average spectrum as the standard spectrum.The parameters of the SAM model,which had the best classification results with 3-point smoothing,zero-order derivative,and 6-degrees spectral angle,was determined to achieve the accurate identification of the background,weeds,and cabbages.In comparison,the SAM model can completely separate the plants from the soil background but not perfect for weeds to be separated from the cabbages.In conclusion,the SAM classification model with the HSI analyzer could completely distinguish weeds from background and cabbages.

Method for UAV spraying pattern measurement with PLS model based spectrum analysis

Unmanned aerial vehicle(UAV)chemical application is widely used for crop protection,and spraying pattern is one of the most important factors that influence the chemical control efficacy.A method for UAV spraying pattern measurement with partial least squares(PLS)model based spectrum analysis was proposed in this study to measure the UAV spraying pattern more accurately.The method involved the steps of fluorescent tracer solution spray and its droplets collection,the spectrum on paper strip acquiring,spectrum processing and analysis,PLS modeling.In order to verify the applicability of the method and obtain the parameters of the PLS model,UAV spraying experiments were performed in the field.Then Fluorescent tracer solution was sprayed and its droplets are collected by paper strip,and the original spectrum on paper strip obtained by a spectrometer was processed by the Savitzky-Golay and standard normalized variable(SNV)method.The prediction model of coverage rate selected as the droplet deposition parameter to measure the UAV spraying pattern,was established by using PLS method.To verify the superiority of the PLS model,a traditional linear regression(LR)model of coverage rate was established simultaneously.The results demonstrate that the method with PLS model based spectrum analysis can measure the UAV spraying pattern effectively,and PLS model has a better performance of RV2=0.94 and RMSEP=0.9446 than that of the LR model.

Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data

Aerial multispectral images are a good source of crop,soil,and ground coverage information.Spectral reflectance indices provide a useful tool for monitoring crop growing status.A series of aerial images were obtained by an airborne MS4100 multispectral imaging system on the cotton and soybean field.Ground hyperspectral data were acquired with a ground-based integration system at the same time.The Normalized Difference Vegetative Index(NDVI),Simple Ratio(SR),and Soil Adjusted Vegetation Index(SAVI)calculated from both systems were analyzed and compared.The information derived from aerial multispectral images has shown the potential to monitor the general growth status of crop field.The vegetation indices derived from both systems were significantly different(p-value was 0.073 atα=0.1 level)at the early growing stage of crops.The correlation coefficients of the image NDVI and ground NDVI were 0.3029 for soybean field and 0.338 for cotton field.SAVI and SR were not correlated.

Spatial distribution visualization of PWM continuous variable-rate spray

Pesticide application is a dynamic spatial distribution process,in which spray liquid should be able to cover the targets with desired thickness and uniformity.Therefore,it is important to study the 2-D and 3-D(dimensional)spray distribution to evaluate spraying quality.The curve-surface generation methods in Excel were used to establish 1-D,2-D,and 3-D graphics of variable-rate spray distribution in order to characterize the space distribution of the variable-rate spray.The 1-D,2-D,and 3-D distribution graphs of Pulse-Width Modulation(PWM)-based continuous variable-rate spray were developed to provide a tool to analyze the distribution characteristics of the spray.The 1-D graph showed that the spray distribution concentrated toward the center of the spray field with the decreased flow-rate.The 2-D graph showed that the spray distribution always spread as the shape of Normal Probability Distribution with the change of the flow-rate.The 3-D graph showed that the spray distribution tended to be uniform when the sprayer travelled forward at the appropriate speed.This study indicated that the visualization method could be directly used for analysis and comparison of different variable-rate spray distributions from different experimental conditions and measuring methods.

Analysis of variograms with various sample sizes from a multispectral image

Variogram plays a crucial role in remote sensing application and geostatistics.It is very important to estimate variogram reliably from sufficient data.In this study,the analysis of variograms computed on various sample sizes of remotely sensed data was conducted.A 100×100-pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands,Green,Red and near-infrared(NIR).Green,Red,NIR and Normalized Difference Vegetation Index(NDVI)datasets were imported into R software for spatial analysis.Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated.In this case,half size of the subset image data was enough to reliably estimate the variograms for NIR and Red wavebands.To map the variation on NDVI within the weed field,ground sampling interval should be smaller than 12 m.The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study.

Developing a modeling tool for flow profiling in irrigation distribution networks

Efforts are underway to rehabilitate the irrigation districts,such as in the Rio Grande Basin in Texas.Water distribution network models are needed to help prioritize and analyze various rehabilitation options,as well as to scientifically quantify irrigation water demands,usages,and losses,and to help manage gate automation.However,commercially available software packages were limited in applications due to their high cost and operational difficulty.This study aims to develop a modeling tool for modeling the water flow profile in irrigation distribution networks.The goal of developing the modeling tool was to make the modeling process simple,fast,reliable and accurate.On the basis of methodological study,the modeling tool has been developed for branching canal networks with the assumption of steady gradually varied flow.The flow profile calculation of the tool was verified from a single channel with 1%root mean squared error compared to the benchmark calculation and a branching network with 5%to 12%relative errors compared to check point measurement along the network.The developed modeling tool will be able to play an important role in water quantification for planning,analysis and development for modernization of irrigation systems.