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
In the oxidation treatment of textile dyeing sludge,the quantitative and transformation laws of organic chlorine are not clear enough.Thus,this study mainly evaluated the treatment of textile dyeing sludge by Fenton a...In the oxidation treatment of textile dyeing sludge,the quantitative and transformation laws of organic chlorine are not clear enough.Thus,this study mainly evaluated the treatment of textile dyeing sludge by Fenton and Fenton-like system from the aspects of the influence of Cl^(-),the removal of polycyclic aromatic hydrocarbons (PAHs) and organic carbon,and the removal and formation mechanism of organic chlorine.The results showed that the organic halogen in sludge was mainly hydrophobic organic chlorine,and the content of adsorbable organic chlorine (AOCl) was 0.30 mg/g (dry sludge).In the Fenton system with pH=3,500 mg/L Cl-,30 mmol/L Fe^(2+)and 30 mmol/L H_(2)O_(2),the removal of phenanthrene was promoted by chlorine radicals (·Cl),and the AOCl in sludge solid phase increased to 0.55 mg/g (dry sludge) at 30 min.According to spectral analysis,it was found that ·Cl could chlorinate aromatic and aliphatic compounds (excluding PAHs) in solid phase at the same time,and eventually led to the accumulation of aromatic chlorides in solid phase.Strengthening the oxidation ability of Fenton system increased the formation of organic chlorines in liquid and solid phases.In weak acidity,the oxidation and desorption of superoxide anion promoted the removal and migration of PAHs and organic carbon in solid phase,and reduced the formation of total organic chlorine.The Fenton-like system dominated by nonhydroxyl radical could realize the mineralization of PAHs,organic carbon and organic chlorines instead of migration.This paper builds a basis for the selection of sludge conditioning methods.展开更多
In the pre-Qin period,Confucius proposed six subjects namely the etiquette,music,archery,driving,literacy,and calculation.Among the six subjects,music was ranked the second.Among them,traditional education in China ca...In the pre-Qin period,Confucius proposed six subjects namely the etiquette,music,archery,driving,literacy,and calculation.Among the six subjects,music was ranked the second.Among them,traditional education in China can fully reflect the importance of music education,and the essence and core of music education can be reflected from the requirements of aesthetic education.In recent years,with the continuous development and improvement of production and life,the theme of education in today’s society has changed,and quality education is the center and focus of education today.Moreover,people begin to focus on how to inherit and publicize the traditional music culture.As the music culture is of great importance,many people are encouraged to continue to practice and publicize the traditional music.The main point of this article is Confucian theory of music education.展开更多
Folk songs and dances originated from people's sacrificial activities in the struggle against nature in the primitive society.Their origins are related to the ideology and living environment of the people at that ...Folk songs and dances originated from people's sacrificial activities in the struggle against nature in the primitive society.Their origins are related to the ideology and living environment of the people at that period of time.These activities were expressed in the form of primitive songs and dances,and gradually evolved into folk songs and dances.The gar pa song and dance from Diebu,in Gannan region,is a unique song and dance of a Tibetan region on the eastern edge of Qinghai-Tibet Plateau.Its content and form are unique.It still retains the original trinity feature which includes poem,music,and dance.The production of songs and dances contains rich cultural connotations and unique local characteristics.This article elaborates the characteristics of Diebu's gar pa song and dance in terms of its music and performance form.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
A detection method based on transmittance spectroscopy and support vector machine(SVM)was proposed to achieve rapid nondestructive detection of moldy core in apples.A visible to near-infrared(Vis/NIR)spectroradiometer...A detection method based on transmittance spectroscopy and support vector machine(SVM)was proposed to achieve rapid nondestructive detection of moldy core in apples.A visible to near-infrared(Vis/NIR)spectroradiometer was used for scanning transmittance spectra of 215 apple samples in the wavelength range of 200-1025 nm.Wavelet transform was used to reduce the dimensionality of the spectra and extract wavelet coefficients.Two classification algorithms including artificial neural network(ANN)and SVM were used to develop models whose parameters were optimized by genetic algorithms(GA)for determination of the presence and types of moldy core in apples.Comparisons results of the models showed that the GA-SVM model obtained the optimal result with an accuracy of 96.92%for detecting the presence of moldy core and 81.48%for distinguishing symptom types of the disease.These results indicate that it is feasible to detect moldy core in apples nondestructively and rapidly based on transmittance spectroscopy and that wavelet transform is an effective method for extraction of characteristics from spectra.Moreover,the GA-SVM algorithm in conjunction with Vis/NIR transmittance spectroscopy can accurately achieve fast and nondestructive detection of the presence and types of moldy core in apples.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
Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper present...Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.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
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
Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulti...Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.展开更多
On behalf of the Association of the Overseas Chinese Agricultural,Biological and Food Engineers(AOCABFE or AOC),I would like to warmly and sincerely congratulate on the publishing of the first issue of the Internation...On behalf of the Association of the Overseas Chinese Agricultural,Biological and Food Engineers(AOCABFE or AOC),I would like to warmly and sincerely congratulate on the publishing of the first issue of the International Journal of Agricultural and Biological Engineering(IJABE).This represents another successful collaboration between AOC and the Chinese Society of Agricultural Engineering(CSAE).This newly launched journal will provide the scientific community and interested readers with latest research accomplishments and applications in six broadly defined technical divisions of the agricultural and biological engineering field.This is a significant contribution to the agricultural and biological engineering profession both in China and in the world.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.
基金supported by the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (No.2017BT01Z032)the National Natural Science Foundation of China (No.21577027)。
文摘In the oxidation treatment of textile dyeing sludge,the quantitative and transformation laws of organic chlorine are not clear enough.Thus,this study mainly evaluated the treatment of textile dyeing sludge by Fenton and Fenton-like system from the aspects of the influence of Cl^(-),the removal of polycyclic aromatic hydrocarbons (PAHs) and organic carbon,and the removal and formation mechanism of organic chlorine.The results showed that the organic halogen in sludge was mainly hydrophobic organic chlorine,and the content of adsorbable organic chlorine (AOCl) was 0.30 mg/g (dry sludge).In the Fenton system with pH=3,500 mg/L Cl-,30 mmol/L Fe^(2+)and 30 mmol/L H_(2)O_(2),the removal of phenanthrene was promoted by chlorine radicals (·Cl),and the AOCl in sludge solid phase increased to 0.55 mg/g (dry sludge) at 30 min.According to spectral analysis,it was found that ·Cl could chlorinate aromatic and aliphatic compounds (excluding PAHs) in solid phase at the same time,and eventually led to the accumulation of aromatic chlorides in solid phase.Strengthening the oxidation ability of Fenton system increased the formation of organic chlorines in liquid and solid phases.In weak acidity,the oxidation and desorption of superoxide anion promoted the removal and migration of PAHs and organic carbon in solid phase,and reduced the formation of total organic chlorine.The Fenton-like system dominated by nonhydroxyl radical could realize the mineralization of PAHs,organic carbon and organic chlorines instead of migration.This paper builds a basis for the selection of sludge conditioning methods.
基金This article is originated from the 2019 Gansu Provincial Higher Education Project on Innovation Ability Improvement,Project Number:2019A-124。
文摘In the pre-Qin period,Confucius proposed six subjects namely the etiquette,music,archery,driving,literacy,and calculation.Among the six subjects,music was ranked the second.Among them,traditional education in China can fully reflect the importance of music education,and the essence and core of music education can be reflected from the requirements of aesthetic education.In recent years,with the continuous development and improvement of production and life,the theme of education in today’s society has changed,and quality education is the center and focus of education today.Moreover,people begin to focus on how to inherit and publicize the traditional music culture.As the music culture is of great importance,many people are encouraged to continue to practice and publicize the traditional music.The main point of this article is Confucian theory of music education.
文摘Folk songs and dances originated from people's sacrificial activities in the struggle against nature in the primitive society.Their origins are related to the ideology and living environment of the people at that period of time.These activities were expressed in the form of primitive songs and dances,and gradually evolved into folk songs and dances.The gar pa song and dance from Diebu,in Gannan region,is a unique song and dance of a Tibetan region on the eastern edge of Qinghai-Tibet Plateau.Its content and form are unique.It still retains the original trinity feature which includes poem,music,and dance.The production of songs and dances contains rich cultural connotations and unique local characteristics.This article elaborates the characteristics of Diebu's gar pa song and dance in terms of its music and performance form.
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National High-tech Research and Development Projects(863)(2013AA10230402)National Natural Science Foundation of China(61473235)the Major Pilot Projects of the Agro-Tech Extension and Service in Shaanxi(2016XXPT-05).
文摘A detection method based on transmittance spectroscopy and support vector machine(SVM)was proposed to achieve rapid nondestructive detection of moldy core in apples.A visible to near-infrared(Vis/NIR)spectroradiometer was used for scanning transmittance spectra of 215 apple samples in the wavelength range of 200-1025 nm.Wavelet transform was used to reduce the dimensionality of the spectra and extract wavelet coefficients.Two classification algorithms including artificial neural network(ANN)and SVM were used to develop models whose parameters were optimized by genetic algorithms(GA)for determination of the presence and types of moldy core in apples.Comparisons results of the models showed that the GA-SVM model obtained the optimal result with an accuracy of 96.92%for detecting the presence of moldy core and 81.48%for distinguishing symptom types of the disease.These results indicate that it is feasible to detect moldy core in apples nondestructively and rapidly based on transmittance spectroscopy and that wavelet transform is an effective method for extraction of characteristics from spectra.Moreover,the GA-SVM algorithm in conjunction with Vis/NIR transmittance spectroscopy can accurately achieve fast and nondestructive detection of the presence and types of moldy core in apples.
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.
文摘Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.
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 National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402)Agricultural Science and Technology Project of Shaanxi Province(No.2016NY-157)Fundamental Research Funds Central Universities(2452016077).
文摘Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.
文摘On behalf of the Association of the Overseas Chinese Agricultural,Biological and Food Engineers(AOCABFE or AOC),I would like to warmly and sincerely congratulate on the publishing of the first issue of the International Journal of Agricultural and Biological Engineering(IJABE).This represents another successful collaboration between AOC and the Chinese Society of Agricultural Engineering(CSAE).This newly launched journal will provide the scientific community and interested readers with latest research accomplishments and applications in six broadly defined technical divisions of the agricultural and biological engineering field.This is a significant contribution to the agricultural and biological engineering profession both in China and in the world.
