Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existi...Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,theAutoMLforModelCompression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.展开更多
Genetic transformation is widely used to improve target traits and to study gene function in wheat.However,transformation efficiency depends on the physiological status of the recipient genotype and that is affected b...Genetic transformation is widely used to improve target traits and to study gene function in wheat.However,transformation efficiency depends on the physiological status of the recipient genotype and that is affected by several factors including powdery mildew(PM)infection.The widely used recipient variety Fielder is very susceptible to PM.Therefore,it would be beneficial to develop PM resistant derivatives with high regeneration ability for use in genetic transformation.In the present study PM resistant lines CB037 and Pm97033 carrying genes Pm21 and PmV,respectively,were backcrossed to Fielder with selection for PM resistance.Five lines,NT89,NT90,NT154,and WT48 with Pm21 and line FL347 with PmV were developed,identified by molecular markers and genomic in situ hybridization(GISH)or fluorescent in situ hybridization(FISH),and further subjected to detailed assessment of agronomic traits and regeneration ability following genetic transformation capacity.Lines FL347,WT48,NT89 and NT154 assessed as being equal to,or superior,to Fielder in regeneration and transformation ability are recommended as suitable materials for the replacement of Fielder for wheat gene transfer and genome editing study.展开更多
基金supported by National Key Research and Development Program(No.2016YFD0201305-07)Guizhou Provincial Basic Research Program(Natural Science)(No.ZK[2023]060)Open Fund Project in Semiconductor Power Device Reliability Engineering Center of Ministry of Education(No.ERCMEKFJJ2019-06).
文摘Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,theAutoMLforModelCompression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.
基金supported by the National Natural Science Foundation of China(31971945).
文摘Genetic transformation is widely used to improve target traits and to study gene function in wheat.However,transformation efficiency depends on the physiological status of the recipient genotype and that is affected by several factors including powdery mildew(PM)infection.The widely used recipient variety Fielder is very susceptible to PM.Therefore,it would be beneficial to develop PM resistant derivatives with high regeneration ability for use in genetic transformation.In the present study PM resistant lines CB037 and Pm97033 carrying genes Pm21 and PmV,respectively,were backcrossed to Fielder with selection for PM resistance.Five lines,NT89,NT90,NT154,and WT48 with Pm21 and line FL347 with PmV were developed,identified by molecular markers and genomic in situ hybridization(GISH)or fluorescent in situ hybridization(FISH),and further subjected to detailed assessment of agronomic traits and regeneration ability following genetic transformation capacity.Lines FL347,WT48,NT89 and NT154 assessed as being equal to,or superior,to Fielder in regeneration and transformation ability are recommended as suitable materials for the replacement of Fielder for wheat gene transfer and genome editing study.
