In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees wi...In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.展开更多
Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation.In recent studies,phenotyping of rice panicles during the heading–flowering stage still lacks co...Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation.In recent studies,phenotyping of rice panicles during the heading–flowering stage still lacks comprehensive analysis,especially of panicle development under different nitrogen treatments.In this work,we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5,ResNet50,and DeepSORT models.Combined with field observation data,the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments.The result shows that panicle counting throughout the heading–flowering stage achieved high accuracy(R^(2)=0.96 and RMSE=1.73),and heading date was estimated with an absolute error of 0.25 days.In addition,by identical panicle tracking based on the time-series images,we analyzed detailed flowering phenotypic changes of a single panicle,such as flowering duration and individual panicle flowering time.For rice population,with an increase in the nitrogen application:panicle number increased,heading date changed little,but the duration was slightly extended;cumulative flowering panicle number increased,rice flowering initiation date arrived earlier while the ending date was later;thus,the flowering duration became longer.For a single panicle,identical panicle tracking revealed that higher nitrogen application led to earlier flowering initiation date,significantly longer flowering days,and significantly longer total duration from vigorous flowering beginning to the end(total DBE).However,the vigorous flowering beginning time showed no significant differences and there was a slight decrease in daily DBE.展开更多
Dust and Sand Storms （DSS） originating in deserts in arid and semi-arid regions are events raising global public concern. An important component of atmospheric aerosols, dust aerosols play a key role in climatic and...Dust and Sand Storms （DSS） originating in deserts in arid and semi-arid regions are events raising global public concern. An important component of atmospheric aerosols, dust aerosols play a key role in climatic and environmental changes at the regional and the global scale. Deserts and semi-deserts are the main source of dust and sand, but regions that undergo vegetation deterioration and desertification due to climate change and human activities also contribute significantly to DSS. Dust aerosols are mainly composed of dust particles with an average diameter of 2 l.tm, which can be transported over thousands of kilometers. Dust aerosols influence the radiation budget of the earth- atmosphere system by scattering solar short-wave radiation and absorbing surface long-wave radiation. They can also change albedo and rainfall patterns because they can act as cloud condensation nuclei （CCN） or ice nuclei （IN）. Dust deposition is an important source of both marine nutrients and contaminants. Dust aerosols that enter marine ecosystems after long-distance transport influence phytoplankton biomass in the oceans, and thus global climate by altering the amount of CO2 absorbed by phytoplankton. In addition, the carbonates carried by dust aerosols are an important source of carbon for the alkaline carbon pool, which can buffer atmospheric acidity and increase the alkalinity of seawater. DSS have both positive and negative impacts on human society： they can exert adverse impacts on human＇s living environment, but can also contribute to the mitigation of global warming and the reduction of atmospheric acidity.展开更多
Microplot extraction(PE)is a necessary image processing step in unmanned aerial vehicle-(UAV-)based research on breeding fields.At present,it is manually using ArcGIS,QGIS,or other GIS-based software,but achieving the...Microplot extraction(PE)is a necessary image processing step in unmanned aerial vehicle-(UAV-)based research on breeding fields.At present,it is manually using ArcGIS,QGIS,or other GIS-based software,but achieving the desired accuracy is timeconsuming.We therefore developed an intuitive,easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions.The program uses four major steps:(1)binary segmentation,(2)microplot extraction,(3)production of∗.shp files to enable further file manipulation,and(4)projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality.Crop rows were successfully identified in all trial fields.The performance of the proposed method was evaluated by calculating the intersection-over-union(IOU)ratio between microplots determined manually and by Easy MPE:the average IOU(±SD)of all trials was 91%(±3).展开更多
基金This study was partially funded by the“Collaboration Research Program of IDEAS”,Chubu University(IDEAS 201603 and IDEAS201702)the CREST Program“Knowledge Discovery by Constructing AgriBigData”(JPMJCR1512)the SICORP Program“Data Science-based Farming Support System for Sustainable Crop Production under Climatic Change”of the Japan Science and Technology Agency.
文摘In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.
基金supported by the Hainan Yazhou Bay Seed Lab in Hainan Province(Grant No.B21HJ1005)the National Key R&D Program of China(No.2022YFE0116200)the“JBGS”Project of Seed Industry Revitalization in Jiangsu Province(JBGS[2021]007).
文摘Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation.In recent studies,phenotyping of rice panicles during the heading–flowering stage still lacks comprehensive analysis,especially of panicle development under different nitrogen treatments.In this work,we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5,ResNet50,and DeepSORT models.Combined with field observation data,the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments.The result shows that panicle counting throughout the heading–flowering stage achieved high accuracy(R^(2)=0.96 and RMSE=1.73),and heading date was estimated with an absolute error of 0.25 days.In addition,by identical panicle tracking based on the time-series images,we analyzed detailed flowering phenotypic changes of a single panicle,such as flowering duration and individual panicle flowering time.For rice population,with an increase in the nitrogen application:panicle number increased,heading date changed little,but the duration was slightly extended;cumulative flowering panicle number increased,rice flowering initiation date arrived earlier while the ending date was later;thus,the flowering duration became longer.For a single panicle,identical panicle tracking revealed that higher nitrogen application led to earlier flowering initiation date,significantly longer flowering days,and significantly longer total duration from vigorous flowering beginning to the end(total DBE).However,the vigorous flowering beginning time showed no significant differences and there was a slight decrease in daily DBE.
基金Acknowledgements This study was supported by the National Special Scientific Research Fund with Public Welfare in Forestry Field （Grant No. 201404304-4）, the National Natural Science Foundation of China （Grant Nos. 31570710 and 31100518）, the National Key Research and Development Program of China （Grant No. 2016YFC0500801-03）, and the Lecture and Study Program for Outstanding Scholars from Home and Abroad （Grant No. CAFYBB2011007）. CGS acknowledges the financial support of NASA Headquarters under the NASA Earth and Space Science Fellowship Program （Grant No. 14-EARTH14F-241） and of the Science, Technology, and Environmental Policy Fellowship from the Princeton Environmental Institute.
文摘Dust and Sand Storms （DSS） originating in deserts in arid and semi-arid regions are events raising global public concern. An important component of atmospheric aerosols, dust aerosols play a key role in climatic and environmental changes at the regional and the global scale. Deserts and semi-deserts are the main source of dust and sand, but regions that undergo vegetation deterioration and desertification due to climate change and human activities also contribute significantly to DSS. Dust aerosols are mainly composed of dust particles with an average diameter of 2 l.tm, which can be transported over thousands of kilometers. Dust aerosols influence the radiation budget of the earth- atmosphere system by scattering solar short-wave radiation and absorbing surface long-wave radiation. They can also change albedo and rainfall patterns because they can act as cloud condensation nuclei （CCN） or ice nuclei （IN）. Dust deposition is an important source of both marine nutrients and contaminants. Dust aerosols that enter marine ecosystems after long-distance transport influence phytoplankton biomass in the oceans, and thus global climate by altering the amount of CO2 absorbed by phytoplankton. In addition, the carbonates carried by dust aerosols are an important source of carbon for the alkaline carbon pool, which can buffer atmospheric acidity and increase the alkalinity of seawater. DSS have both positive and negative impacts on human society： they can exert adverse impacts on human＇s living environment, but can also contribute to the mitigation of global warming and the reduction of atmospheric acidity.
基金This work was partly funded by the CREST Program“Knowledge Discovery by Constructing AgriBigData”(JPMJCR1512)the SICORP Program“Data Science-Based Farming Support System for Sustainable Crop Production under Climatic Change”of the Japan Science and Technology Agency and the“Smart-Breeding System for Innovative Agriculture (BAC3001)”of the Ministry of Agriculture,Forestry and Fisheries of Japan.
文摘Microplot extraction(PE)is a necessary image processing step in unmanned aerial vehicle-(UAV-)based research on breeding fields.At present,it is manually using ArcGIS,QGIS,or other GIS-based software,but achieving the desired accuracy is timeconsuming.We therefore developed an intuitive,easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions.The program uses four major steps:(1)binary segmentation,(2)microplot extraction,(3)production of∗.shp files to enable further file manipulation,and(4)projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality.Crop rows were successfully identified in all trial fields.The performance of the proposed method was evaluated by calculating the intersection-over-union(IOU)ratio between microplots determined manually and by Easy MPE:the average IOU(±SD)of all trials was 91%(±3).
