[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,...[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,and the aerial data map was converted into geotif image with longitude and latitude and then inputted into the detection system for preprocessing,mainly for image feature extraction and classification.Through deep learning of MATLAB software and BP neural network algorithm,the feature similarity of the images in the established characteristic database of cotton diseases and insect pests was compared.[Results]Through comparative analysis of characteristics of a large number of diseases and insect pests,it was found that deep learning method had high discrimination accuracy and good reliability.[Conclusions]The dynamic detection system using deep learning can well find cotton diseases and insect pests,and achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.展开更多
The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting ...The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting area in XinCheng county of Guangxi Province during 2008-2010,which were coincided with the occurrence periods of related phenology of local Prunus persica Rootstock.With P.persica Rootstock as indicator plant,the occurrence periods of three species of pests and one species of disease were predicted,respectively,and the method was simple and accurate,which could be the foundation for preventing these pests and diseases in the local field.展开更多
Through on-the-spot investigation and survey in Xinjiang cotton region and relevant data( containing statistical data,online information and government documents,etc.),the characteristics of water resource,wind-sand d...Through on-the-spot investigation and survey in Xinjiang cotton region and relevant data( containing statistical data,online information and government documents,etc.),the characteristics of water resource,wind-sand disaster,soil salinization,cotton diseases,insect pests and weeds in Xinjiang are studied. It is proposed reasons and specific programmes of green development strategy of Xinjiang cotton,and specific strategies contain ecological water supply,land desertification control,soil improvement,and ecological control of diseases,insect pests and weeds,thereby providing the support for sustainable development of Xinjiang cotton.展开更多
基金Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region(2020D01C003)。
文摘[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,and the aerial data map was converted into geotif image with longitude and latitude and then inputted into the detection system for preprocessing,mainly for image feature extraction and classification.Through deep learning of MATLAB software and BP neural network algorithm,the feature similarity of the images in the established characteristic database of cotton diseases and insect pests was compared.[Results]Through comparative analysis of characteristics of a large number of diseases and insect pests,it was found that deep learning method had high discrimination accuracy and good reliability.[Conclusions]The dynamic detection system using deep learning can well find cotton diseases and insect pests,and achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.
基金Supported by Natural Scientific Research Topics of Guangxi Scienceand Technology Department(GKG0992003B-40)Natural Scientific Research Topics of Guangxi Education Department(GJKY200809MS196)~~
文摘The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting area in XinCheng county of Guangxi Province during 2008-2010,which were coincided with the occurrence periods of related phenology of local Prunus persica Rootstock.With P.persica Rootstock as indicator plant,the occurrence periods of three species of pests and one species of disease were predicted,respectively,and the method was simple and accurate,which could be the foundation for preventing these pests and diseases in the local field.
基金Supported by National Key R&D Program of China(2017YFD0201900)Special Project of Key R&D Tasks in Xinjiang Uygur Autonomous Region(2016B01001-2)+1 种基金National Technical System of Cotton Industry,Ministry of Agriculture(CARS-18-07)Foundation of Key Laboratory of Desert Oasis Crop Physiology,Ecology and Farming,Ministry of Agriculture
文摘Through on-the-spot investigation and survey in Xinjiang cotton region and relevant data( containing statistical data,online information and government documents,etc.),the characteristics of water resource,wind-sand disaster,soil salinization,cotton diseases,insect pests and weeds in Xinjiang are studied. It is proposed reasons and specific programmes of green development strategy of Xinjiang cotton,and specific strategies contain ecological water supply,land desertification control,soil improvement,and ecological control of diseases,insect pests and weeds,thereby providing the support for sustainable development of Xinjiang cotton.
