Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical presc...Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application maps.However,the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and analysis.Manual methods can be subjec-tive as well.Therefore,this study explores aerial photogrammetry based method of tree–row–volume(TRV),leaf–wall–area(LWA),canopy volume(CV)and canopy cover(CC)esti-mation for grapevine and apple canopies.Remote sensing data was collected using a con-sumer–grade small unmanned aerial system(UAS)with an RGB imaging sensor flying at different flight altitudes i.e.,15 m(Ground sampling distance,GSD=0.45 cm pixel^(-1) at 65°sensor inclination),30 m(0.90 and 0.85 cm pixel^(-1) at 65°and 75°,respectively),45 m(1.35 and 1.27 cm pixel^(-1) at 65°and 75°,respectively)and 60 m(1.81 and 1.69 cm pixel^(-1) at 65°and 75°,respectively).Crop surface model(CSM)was derived from such data to esti-mate canopy height,width and foliage vigor,which are further used to estimate TRV,LWA,CV and CC.The ground measured and aerial imagery estimated TRV had a strong relation-ship with the data collected at the lowest GSD within grapevine canopies(R^(2)=0.77 at 0.45 cm pixel^(-1)as well as for apple canopies(R^(2)=0.82 at 0.90 cm pixel^(-1).Similar trends were observed for the LWA(R^(2)=0.77 and 0.86),CV(R^(2)=0.43 and 0.64)and CC(R^(2)=0.61 and 0.68)estimates for grapevine and apple canopies,respectively.Increasing GSD(≥0.45 cm pixel^(-1) in grapevine and≥0.90 cm pixel^(-1) in apple)resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines(R^(2)=0.36)and apple canopies(R^(2)=0.39–0.78).Overall,the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65°orientation aided in the development of site–specific high–quality canopy vigor maps that can be used in precision crop inputs management related decision making.展开更多
文摘Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application maps.However,the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and analysis.Manual methods can be subjec-tive as well.Therefore,this study explores aerial photogrammetry based method of tree–row–volume(TRV),leaf–wall–area(LWA),canopy volume(CV)and canopy cover(CC)esti-mation for grapevine and apple canopies.Remote sensing data was collected using a con-sumer–grade small unmanned aerial system(UAS)with an RGB imaging sensor flying at different flight altitudes i.e.,15 m(Ground sampling distance,GSD=0.45 cm pixel^(-1) at 65°sensor inclination),30 m(0.90 and 0.85 cm pixel^(-1) at 65°and 75°,respectively),45 m(1.35 and 1.27 cm pixel^(-1) at 65°and 75°,respectively)and 60 m(1.81 and 1.69 cm pixel^(-1) at 65°and 75°,respectively).Crop surface model(CSM)was derived from such data to esti-mate canopy height,width and foliage vigor,which are further used to estimate TRV,LWA,CV and CC.The ground measured and aerial imagery estimated TRV had a strong relation-ship with the data collected at the lowest GSD within grapevine canopies(R^(2)=0.77 at 0.45 cm pixel^(-1)as well as for apple canopies(R^(2)=0.82 at 0.90 cm pixel^(-1).Similar trends were observed for the LWA(R^(2)=0.77 and 0.86),CV(R^(2)=0.43 and 0.64)and CC(R^(2)=0.61 and 0.68)estimates for grapevine and apple canopies,respectively.Increasing GSD(≥0.45 cm pixel^(-1) in grapevine and≥0.90 cm pixel^(-1) in apple)resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines(R^(2)=0.36)and apple canopies(R^(2)=0.39–0.78).Overall,the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65°orientation aided in the development of site–specific high–quality canopy vigor maps that can be used in precision crop inputs management related decision making.
