Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some...Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.展开更多
China is one of the largest meat producing countries in the wodd. With the growing concern for food safety more attention has been paid to meat quality. The application of conventional test methods for meat quality is...China is one of the largest meat producing countries in the wodd. With the growing concern for food safety more attention has been paid to meat quality. The application of conventional test methods for meat quality is limited by many factors, and subjectiveness, such as longer time to prepare samples and to test. A sensor matrix was constructed with several separate air sensors, and tests were conducted to detect the freshness of the beef. The results show that the air sensors TGS2610, TGS2600, TGS2611, TGS2620 and TGS2602 made by Tianjin Figaro Electronic Co, Ltd could be used to determine the degree of freshness but TGS2442 is not suitable. This study provides a foundation for designing and making an economical and practical detector for beef freshness.展开更多
Adult corn earworm, Helicoverpa zea (Boddie), feeds on plant exudates soon after emergence from pupa in their natural habitat, and thereafter disperses to suitable host plants for reproduction. The intent of this stud...Adult corn earworm, Helicoverpa zea (Boddie), feeds on plant exudates soon after emergence from pupa in their natural habitat, and thereafter disperses to suitable host plants for reproduction. The intent of this study was to determine if EntrustTM, an organic formulation of spinosad, could be used in a behavioral-based pest management strategy to control H. zea in organic farming systems. In the laboratory, we evaluated the response of the corn earworm to Entrust mixed with sugar solution relative to ingestion, toxicity and proboscis extension. The sucrose solution served as a feeding stimulant and simulated the plant exudate. Lethal concentration of Entrust (LC50 with 95% CLs) for male corn earworm captured in pheromone-baited traps was 0.48 (0.43 - 0.53) mgL-1 for 24 h response. Mean lethal time was 2.56 ± 0.13 h with ingestion of Entrust at 50 mg·L-1. A lethal dose of Entrust at 1000 mg·L-1 inhibited neither ingestion nor proboscis extension response of the insect. A detailed study of the adult corn earworm in the laboratory relative to toxicity after ingestion of Entrust indicates that the pesticide has potential to control the insect when used in an insecticidal bait formulation as part of an attract-and- kill system. Field studies are needed to support the conclusion.展开更多
With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction,unmanned aerial vehicle(UAV)-based low-altitude remote sensing has shown remarkable advant...With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction,unmanned aerial vehicle(UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments.However,the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images.To address these challenges,we developed the Integrated High-Throughput Universal Phenotyping(IHUP)software as a data producer and study accelerator that included 4 functional modules:preprocessing,data extraction,data management,and data analysis.Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing.Within a graphical user interface,users can compute image feature information,structural traits,and vegetation indices(Vis),which are indicators of morphological and biochemical traits,in an integrated and high-throughput manner.To fulfill data requirements for different crops,extraction methods such as VI calculation formulae can be customized.To demonstrate and test the composition and performance of the software,we conducted case-related rice drought phenotype monitoring experiments.In combination with a rice leaf rolling score predictive model,leaf rolling score,plant height,VIs,fresh weight,and drought weight were efficiently extracted from multiphase continuous monitoring data.Despite the significant impact of image processing during plot clipping on processing efficiency,the software can extract traits from approximately 500 plots/min in most application cases.The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms,thereby significantly reducing barriers for nonexperts.It holds the promise of significantly accelerating data production in UAV phenotyping experiments.展开更多
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 s...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.展开更多
Aerial spraying plays an important role in promoting agricultural production and protecting the biological environment due to its flexibility,high effectiveness,and large operational area per unit of time.In order to ...Aerial spraying plays an important role in promoting agricultural production and protecting the biological environment due to its flexibility,high effectiveness,and large operational area per unit of time.In order to evaluate the performance parameters of the spraying systems on two fixed wing airplanes M-18B and Thrush 510G,the effective swath width and uniformity of droplet deposition under headwind flight were tested while the planes operated at the altitudes of 5 m and 4 m.The results showed that although wind velocities varied from 0.9 m/s to 4.6 m/s,and the directions of the atomizer switched upward and downward in eight flights,the effective swath widths were kept approximately at 27 m and 15 m for the M-18B and Thrush 510G,respectively,and the latter was more stable.In addition,through analyzing the coefficients of variation(CVs)of droplet distribution,it was found that the CVs of the M-18B were 39.57%,33.54%,47.95%,and 59.04% at wind velocities of 0.9,1.1,1.4 and 4.6 m/s,respectively,gradually enhancing with the increasing of wind speed;the CVs of Thrush 510G were 79.12%,46.19%,14.90%,and 48.69% at wind velocities of 1.3,2.3,3.0 and 3.4 m/s,respectively,which displayed the irregularity maybe due to change of instantaneous wind direction.Moreover,in terms of the CVs and features of droplet distribution uniformity for both airplanes in the spray swath,choosing smaller CV(20%-45%)as the standard of estimation,it was found that the Thrush 510G had a better uniform droplet distribution than the M-18B.The results provide a research foundation for promoting the development of aerial spraying in China.展开更多
Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions.This study employed aerial imagery as a data source and machine...Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions.This study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping area.Comparative analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features(layer spectral,geometry,and texture).Static recognition using layer spectral features had the highest accuracy of 75.4%in object-based classification,and dynamic recognition had the highest accuracy of 88.0%in object-based classification based on layer spectral and geometry features.Dynamic identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results,but also amplify the differences between different features.Object-based classification produced better results than pixel-based classification,and the three feature sets(layer spectral alone,layer spectral and geometry,and all three)resulted in only small differences in accuracy in object-based classification.Dynamic recognition combined with objectbased classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial imagery.The methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.展开更多
Maize(Zea mays L.)is a critical staple crop globally,integral to human consumption,food security,and agricultural product stability.The quality and purity of maize seeds,essential for hybrid seed production,are contin...Maize(Zea mays L.)is a critical staple crop globally,integral to human consumption,food security,and agricultural product stability.The quality and purity of maize seeds,essential for hybrid seed production,are contingent upon effective detasseling.This study investigates the evolution of detasseling technologies and their application in Chinese maize hybrid seed production,with a comparative analysis against the United States.A comprehensive examination of the development and utilization of detasseling technology in Chinese maize hybrid seed production was undertaken,with a specific focus on key milestones.Data from the United States were included for comparative purposes.The analysis encompassed various detasseling methods,including manual,semi-mechanized,and cytoplasmic male sterility,as well as more recent innovations such as detasseling machines,and the emerging field of intelligent detasseling driven by unmanned aerial vehicles(UAVs),computer vision,and mechanical arms.Mechanized detasseling methods were predominantly employed by America.Despite the challenges of inflexible and occasionally overlooked,applying detasseling machines is efficient and reliable.At present,China’s detasseling operations in hybrid maize seed production are mainly carried out by manual work,which is labor-intensive and inefficient.In order to address this issue,China is dedicated to developing intelligent detasseling technology.This study emphasizes the critical role of detasseling in hybrid maize seed production.The United States has embraced mechanized detasseling.The application and development of manual and mechanized detasseling were applied later than those in the United States,but latest intelligent detasseling technologies first appeared in China.Intelligent detasseling is expected to be the future direction,ensuring the quality and efficiency of hybrid maize seed production,with implications for global food security.展开更多
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 i...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.展开更多
The effect of terrain shadow, including the self and cast shadows, is one ofthe main obstacles for accurate retrieval of vegetation parameters byremote sensing in rugged terrains. A shadow- eliminated vegetation index...The effect of terrain shadow, including the self and cast shadows, is one ofthe main obstacles for accurate retrieval of vegetation parameters byremote sensing in rugged terrains. A shadow- eliminated vegetation index(SEVI) was developed, which was computed from only red and nearinfrared top-of-atmosphere reflectance without other heterogeneous dataand topographic correction. After introduction of the conceptual modeland feature analysis of conventional wavebands, the SEVI was constructedby ratio vegetation index (RVI), shadow vegetation index (SVI) andadjustment factor (f (Δ)). Then three methods were used to validate theSEVI accuracy in elimination of terrain shadow effects, including relativeerror analysis, correlation analysis between the cosine of solar incidenceangle (cosi) and vegetation indices, and comparison analysis between SEVIand conventional vegetation indices with topographic correction. Thevalidation results based on 532 samples showed that the SEVI relativeerrors for self and cast shadows were 4.32% and 1.51% respectively. Thecoefficient of determination between cosi and SEVI was only 0.032 and thecoefficient of variation (std/mean) for SEVI was 12.59%. The results indicatethat the proposed SEVI effectively eliminated the effect of terrain shadowsand achieved similar or better results than conventional vegetation indiceswith topographic correction.展开更多
Normalized Difference Vegetation Index(NDVI)has been found to have good correlations with many physical properties of soybean surfaces.Due to the factors of air temperature,humidity,solar radiation,soil moisture,etc.,...Normalized Difference Vegetation Index(NDVI)has been found to have good correlations with many physical properties of soybean surfaces.Due to the factors of air temperature,humidity,solar radiation,soil moisture,etc.,NDVI of soybean varies dynamically in a day.The establishment of the soybean NDVI prediction model at different times in a day can effectively modify this variation.The soybean NDVI values are continuously monitored in hours during soybean seeding,flowering&podding and maturating stages by way of Green Seeker.Results show that the trend of NDVI change every day in the three stages is taken on as a reverse parabola.The NDVI value reaches to the maximum at 8 am or 9 am and decreases to its minimum at 2 pm before a moderate rise.A model for intraday and long-term NDVI change for soybean is built.The test of the model with independent data indicates that the precision meets the demands,with the root mean square error(RMSE)of each day being 3.95,5.45 and 2.86 for the seeding stage,the bean podding stage and the maturation period,respectively.The prediction RMSEs of the soybean NDVI model for soybeans of the three stages for the fifth day are 5.75,2.65 and 5.51,respectively and the prediction RMSEs for the sixth day are 9.74,2.82 and 14.04,respectively according to the data from the first four days.展开更多
The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since...The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.展开更多
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 patt...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.展开更多
Pulse-width modulation(PWM)sprays can improve flow accuracy by adjusting duty cycle and frequency signal which accurately controls the relative proportion of opening time of solenoid valve.The objective of this resear...Pulse-width modulation(PWM)sprays can improve flow accuracy by adjusting duty cycle and frequency signal which accurately controls the relative proportion of opening time of solenoid valve.The objective of this research was to determine the impacts of PWM duty cycle and frequency on spray drift characteristic.Spray tests were conducted in a wind tunnel with a PWM variable-rate spraying system.The airborne drift and sediment drift were determined with tracer method,and the drift potential reduction(DPR)compared with reference condition of 100%duty cycle at vertical profile and horizontal planes were calculated,respectively.The results show that,at a given frequency,droplet size decreases with the increase of duty cycle,the main reason is that the liquid does not reach full pattern development at lower duty cycle.Duty cycle has a greater impact than the frequency on spray drift,the influence weights of duty cycle on airborne drift and sediment drift were 88.32%and 77.89%,respectively.At a lower PWM frequency,in addition to the droplet size,the spray drift may be affected by the pulsed spray pattern.From the perspective of reducing spray drift,it is recommended that the PWM duty cycle should be set in the range of 20%-70%to reduce the potential drift in PWM sprays.This research provides a pesticide drift reduction scheme for variable spraying technology,which can serve as a theoretical basis for PWM parameter selection.展开更多
During the chemical application process,droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance.In order to quickly obtain deposition results in the...During the chemical application process,droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance.In order to quickly obtain deposition results in the field,this study proposed a novel system based on surface humidity sensors.The basic principle is to convert the measured physical quantity change into a capacitance change,thereby realizing the physical quantity to electrical signal conversion.An Android application for mobile terminal and the corresponding coordinator were developed,which allowed operators to control multiple sensors simultaneously through the Bluetooth.The soluble tracer detected by spectrophotometer was used to calibrate the system.The obtained results indicated a good correlation between deposition volume and voltage increment output from the newly developed system(R2 of the six nozzles with Dv0.5 ranging from 107.28μm to 396.20μm were 0.8674-0.9729),and a power regression model based on the least squares technique(R2=0.8022)was developed.In the field test,the system exhibited an optimal performance in predicting the deposition volume.Compared with the conventional method of measuring tracer concentration,the deviation was less than 10%.In addition,the system exhibited good fitting curve of the deposition distribution with droplet number results measured by the water sensitive paper method.展开更多
The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs pe...The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.展开更多
文摘Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.
文摘China is one of the largest meat producing countries in the wodd. With the growing concern for food safety more attention has been paid to meat quality. The application of conventional test methods for meat quality is limited by many factors, and subjectiveness, such as longer time to prepare samples and to test. A sensor matrix was constructed with several separate air sensors, and tests were conducted to detect the freshness of the beef. The results show that the air sensors TGS2610, TGS2600, TGS2611, TGS2620 and TGS2602 made by Tianjin Figaro Electronic Co, Ltd could be used to determine the degree of freshness but TGS2442 is not suitable. This study provides a foundation for designing and making an economical and practical detector for beef freshness.
文摘Adult corn earworm, Helicoverpa zea (Boddie), feeds on plant exudates soon after emergence from pupa in their natural habitat, and thereafter disperses to suitable host plants for reproduction. The intent of this study was to determine if EntrustTM, an organic formulation of spinosad, could be used in a behavioral-based pest management strategy to control H. zea in organic farming systems. In the laboratory, we evaluated the response of the corn earworm to Entrust mixed with sugar solution relative to ingestion, toxicity and proboscis extension. The sucrose solution served as a feeding stimulant and simulated the plant exudate. Lethal concentration of Entrust (LC50 with 95% CLs) for male corn earworm captured in pheromone-baited traps was 0.48 (0.43 - 0.53) mgL-1 for 24 h response. Mean lethal time was 2.56 ± 0.13 h with ingestion of Entrust at 50 mg·L-1. A lethal dose of Entrust at 1000 mg·L-1 inhibited neither ingestion nor proboscis extension response of the insect. A detailed study of the adult corn earworm in the laboratory relative to toxicity after ingestion of Entrust indicates that the pesticide has potential to control the insect when used in an insecticidal bait formulation as part of an attract-and- kill system. Field studies are needed to support the conclusion.
基金supported by National Natural Science Foundation of China(grant nos.42171349 and 42271357)the Major Science and Technology Project of Yunnan Province(202402AE090022)the Major science and technology projects of Inner Mongolia Autonomous Region(2021ZD0046).
文摘With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction,unmanned aerial vehicle(UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments.However,the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images.To address these challenges,we developed the Integrated High-Throughput Universal Phenotyping(IHUP)software as a data producer and study accelerator that included 4 functional modules:preprocessing,data extraction,data management,and data analysis.Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing.Within a graphical user interface,users can compute image feature information,structural traits,and vegetation indices(Vis),which are indicators of morphological and biochemical traits,in an integrated and high-throughput manner.To fulfill data requirements for different crops,extraction methods such as VI calculation formulae can be customized.To demonstrate and test the composition and performance of the software,we conducted case-related rice drought phenotype monitoring experiments.In combination with a rice leaf rolling score predictive model,leaf rolling score,plant height,VIs,fresh weight,and drought weight were efficiently extracted from multiphase continuous monitoring data.Despite the significant impact of image processing during plot clipping on processing efficiency,the software can extract traits from approximately 500 plots/min in most application cases.The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms,thereby significantly reducing barriers for nonexperts.It holds the promise of significantly accelerating data production in UAV phenotyping experiments.
基金The authors gratefully acknowledge the support provided by the National Key Research and Development Program of China(Grant No.2016YFD0200606,Grant No.2018YFD0200700)China Agriculture Research System(Grant No.CARS-15-22).
文摘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.
基金funded by the 863 National High-Tech R&D Program of China(Grant No.2012AA101901)National Natural Science Foundation of China(Grant No.41301471)+1 种基金China Postdoctoral Special Foundation(Grant No.2013T60189)International Postdoctoral Exchange Fellowship Program(Grant No.20130043).
文摘Aerial spraying plays an important role in promoting agricultural production and protecting the biological environment due to its flexibility,high effectiveness,and large operational area per unit of time.In order to evaluate the performance parameters of the spraying systems on two fixed wing airplanes M-18B and Thrush 510G,the effective swath width and uniformity of droplet deposition under headwind flight were tested while the planes operated at the altitudes of 5 m and 4 m.The results showed that although wind velocities varied from 0.9 m/s to 4.6 m/s,and the directions of the atomizer switched upward and downward in eight flights,the effective swath widths were kept approximately at 27 m and 15 m for the M-18B and Thrush 510G,respectively,and the latter was more stable.In addition,through analyzing the coefficients of variation(CVs)of droplet distribution,it was found that the CVs of the M-18B were 39.57%,33.54%,47.95%,and 59.04% at wind velocities of 0.9,1.1,1.4 and 4.6 m/s,respectively,gradually enhancing with the increasing of wind speed;the CVs of Thrush 510G were 79.12%,46.19%,14.90%,and 48.69% at wind velocities of 1.3,2.3,3.0 and 3.4 m/s,respectively,which displayed the irregularity maybe due to change of instantaneous wind direction.Moreover,in terms of the CVs and features of droplet distribution uniformity for both airplanes in the spray swath,choosing smaller CV(20%-45%)as the standard of estimation,it was found that the Thrush 510G had a better uniform droplet distribution than the M-18B.The results provide a research foundation for promoting the development of aerial spraying in China.
基金supported by the National Key Research and Development Program(No.2020YFD1100204)the Provincial Key Basic Research Project(No.2019AB002).
文摘Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions.This study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping area.Comparative analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features(layer spectral,geometry,and texture).Static recognition using layer spectral features had the highest accuracy of 75.4%in object-based classification,and dynamic recognition had the highest accuracy of 88.0%in object-based classification based on layer spectral and geometry features.Dynamic identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results,but also amplify the differences between different features.Object-based classification produced better results than pixel-based classification,and the three feature sets(layer spectral alone,layer spectral and geometry,and all three)resulted in only small differences in accuracy in object-based classification.Dynamic recognition combined with objectbased classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial imagery.The methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.
基金supported by the“Jie Bang Gua Shuai”Science and Technology Project of Heilongjiang Province(Grant No.20212XJ05A0204)The Outstanding Scientist Cultivation Project of Beijing Academy of Agriculture and Forestry Sciences(Grant No.JKZX202205)Chen Liping Young Beijing Scholars Project.
文摘Maize(Zea mays L.)is a critical staple crop globally,integral to human consumption,food security,and agricultural product stability.The quality and purity of maize seeds,essential for hybrid seed production,are contingent upon effective detasseling.This study investigates the evolution of detasseling technologies and their application in Chinese maize hybrid seed production,with a comparative analysis against the United States.A comprehensive examination of the development and utilization of detasseling technology in Chinese maize hybrid seed production was undertaken,with a specific focus on key milestones.Data from the United States were included for comparative purposes.The analysis encompassed various detasseling methods,including manual,semi-mechanized,and cytoplasmic male sterility,as well as more recent innovations such as detasseling machines,and the emerging field of intelligent detasseling driven by unmanned aerial vehicles(UAVs),computer vision,and mechanical arms.Mechanized detasseling methods were predominantly employed by America.Despite the challenges of inflexible and occasionally overlooked,applying detasseling machines is efficient and reliable.At present,China’s detasseling operations in hybrid maize seed production are mainly carried out by manual work,which is labor-intensive and inefficient.In order to address this issue,China is dedicated to developing intelligent detasseling technology.This study emphasizes the critical role of detasseling in hybrid maize seed production.The United States has embraced mechanized detasseling.The application and development of manual and mechanized detasseling were applied later than those in the United States,but latest intelligent detasseling technologies first appeared in China.Intelligent detasseling is expected to be the future direction,ensuring the quality and efficiency of hybrid maize seed production,with implications for global food security.
文摘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.
基金China National Key Research and Development Plan[grant number 2017YFB0504203]China Scholarship Fund[grant number 201706655028]Natural Science Foundation of Fujian Province[grant number 2017J01658].
文摘The effect of terrain shadow, including the self and cast shadows, is one ofthe main obstacles for accurate retrieval of vegetation parameters byremote sensing in rugged terrains. A shadow- eliminated vegetation index(SEVI) was developed, which was computed from only red and nearinfrared top-of-atmosphere reflectance without other heterogeneous dataand topographic correction. After introduction of the conceptual modeland feature analysis of conventional wavebands, the SEVI was constructedby ratio vegetation index (RVI), shadow vegetation index (SVI) andadjustment factor (f (Δ)). Then three methods were used to validate theSEVI accuracy in elimination of terrain shadow effects, including relativeerror analysis, correlation analysis between the cosine of solar incidenceangle (cosi) and vegetation indices, and comparison analysis between SEVIand conventional vegetation indices with topographic correction. Thevalidation results based on 532 samples showed that the SEVI relativeerrors for self and cast shadows were 4.32% and 1.51% respectively. Thecoefficient of determination between cosi and SEVI was only 0.032 and thecoefficient of variation (std/mean) for SEVI was 12.59%. The results indicatethat the proposed SEVI effectively eliminated the effect of terrain shadowsand achieved similar or better results than conventional vegetation indiceswith topographic correction.
基金supported by Governmental Public Industry Research Special Funds for Projects(201301016)National Key Technology R&D Program of the Ministry of Science and Technology(2012BAH29B04-02)+1 种基金Chinese Universities Scientific Fund,Northwest A&F University(QN2011132)the Introduction of Intelligence from Abroad Project for Innovation in Academic Schools,China(B12007).
文摘Normalized Difference Vegetation Index(NDVI)has been found to have good correlations with many physical properties of soybean surfaces.Due to the factors of air temperature,humidity,solar radiation,soil moisture,etc.,NDVI of soybean varies dynamically in a day.The establishment of the soybean NDVI prediction model at different times in a day can effectively modify this variation.The soybean NDVI values are continuously monitored in hours during soybean seeding,flowering&podding and maturating stages by way of Green Seeker.Results show that the trend of NDVI change every day in the three stages is taken on as a reverse parabola.The NDVI value reaches to the maximum at 8 am or 9 am and decreases to its minimum at 2 pm before a moderate rise.A model for intraday and long-term NDVI change for soybean is built.The test of the model with independent data indicates that the precision meets the demands,with the root mean square error(RMSE)of each day being 3.95,5.45 and 2.86 for the seeding stage,the bean podding stage and the maturation period,respectively.The prediction RMSEs of the soybean NDVI model for soybeans of the three stages for the fifth day are 5.75,2.65 and 5.51,respectively and the prediction RMSEs for the sixth day are 9.74,2.82 and 14.04,respectively according to the data from the first four days.
文摘The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.
基金This study was supported by Zhang Ruirui's Beijing Nova Program(No.Z181100006218029)National Natural Science Foundation of China(31601228)+1 种基金BAAFS'Innovation Ability Construction Program 2018(No.KJCX20180424)National Key R&D Program of China(2016YFD0200701-2).
文摘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.
基金The authors acknowledge that this research was financially supported by National Key R&D Program of China(2019YFD1101102-3)National Natural Science Foundation of China(32071907)+1 种基金Outstanding Scientist Cultivation Project of Beijing Academy of Agriculture and Forestry Sciences(JKZX202205)Qingyuan Smart Agriculture Research Institute+New R&D Institutions Construction in North and West Guangdong(2019B090905006).
文摘Pulse-width modulation(PWM)sprays can improve flow accuracy by adjusting duty cycle and frequency signal which accurately controls the relative proportion of opening time of solenoid valve.The objective of this research was to determine the impacts of PWM duty cycle and frequency on spray drift characteristic.Spray tests were conducted in a wind tunnel with a PWM variable-rate spraying system.The airborne drift and sediment drift were determined with tracer method,and the drift potential reduction(DPR)compared with reference condition of 100%duty cycle at vertical profile and horizontal planes were calculated,respectively.The results show that,at a given frequency,droplet size decreases with the increase of duty cycle,the main reason is that the liquid does not reach full pattern development at lower duty cycle.Duty cycle has a greater impact than the frequency on spray drift,the influence weights of duty cycle on airborne drift and sediment drift were 88.32%and 77.89%,respectively.At a lower PWM frequency,in addition to the droplet size,the spray drift may be affected by the pulsed spray pattern.From the perspective of reducing spray drift,it is recommended that the PWM duty cycle should be set in the range of 20%-70%to reduce the potential drift in PWM sprays.This research provides a pesticide drift reduction scheme for variable spraying technology,which can serve as a theoretical basis for PWM parameter selection.
基金supported by the National Key Research and Development Project of China(Grant No.2019YFD1101102-3)Youth Research Fund of Beijing Academy of Agriculture and Forestry Sciences(Grant No.QNJJ202009)+2 种基金Outstanding Scientist Cultivation Project of Beijing Academy of Agriculture and Forestry Sciences(Grant No.JKZX201903)National Natural Science Foundation of China(Grant No.32071907)and Outstanding Young Talents Projects of Beijing Academy of Agriculture and Forestry Sciences-Research on positioning and control technology and equipment of unmanned vehicles in orchards.Also,Shuaihui Feng,Shufan Chai,Mingjia Zhang and Jiaxing Song’s contributions to this experimental work are highly appreciated.
文摘During the chemical application process,droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance.In order to quickly obtain deposition results in the field,this study proposed a novel system based on surface humidity sensors.The basic principle is to convert the measured physical quantity change into a capacitance change,thereby realizing the physical quantity to electrical signal conversion.An Android application for mobile terminal and the corresponding coordinator were developed,which allowed operators to control multiple sensors simultaneously through the Bluetooth.The soluble tracer detected by spectrophotometer was used to calibrate the system.The obtained results indicated a good correlation between deposition volume and voltage increment output from the newly developed system(R2 of the six nozzles with Dv0.5 ranging from 107.28μm to 396.20μm were 0.8674-0.9729),and a power regression model based on the least squares technique(R2=0.8022)was developed.In the field test,the system exhibited an optimal performance in predicting the deposition volume.Compared with the conventional method of measuring tracer concentration,the deviation was less than 10%.In addition,the system exhibited good fitting curve of the deposition distribution with droplet number results measured by the water sensitive paper method.
基金by Cooperative Agreement AP20PPQS&T00C046 from the United States Department of Agriculture's Animal and Plant Health Inspection Service(APHIS).
文摘The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.
