The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on ...The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.展开更多
Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due ...Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.展开更多
[Objectives]To determine the content of mangiferin and homomangiferin in mango leaves by HPLC.[Methods]The mangiferin and homomangiferin were separated and determined by Elite Hypersil C18(5μm,4.6 mm ID×250 mm)c...[Objectives]To determine the content of mangiferin and homomangiferin in mango leaves by HPLC.[Methods]The mangiferin and homomangiferin were separated and determined by Elite Hypersil C18(5μm,4.6 mm ID×250 mm)chromatographic column.Acetonitrile-0.1%(V/V)phosphoric acid solution was used as the mobile phase for gradient elution,the flow rate was 1.0 mL/min,the detection wavelength was 258 nm,the column temperature was 30℃,and the injection volume was 5μL.[Results]There was a good linear relationship between mangiferin and homomangiferin in the range of 0.0254-0.5080μg/μL(r=0.9999)and 0.000960-0.019200μg/μL(r=0.9999),respectively.The average recovery rate(n=6)of mangiferin and homomangiferin in mango leaves was 101.7%(RSD=2.0%)and 101.0%(RSD=1.7%),respectively.[Conclusions]There were great differences in the content of mangiferin and homomangiferin in the leaves of different varieties of mango.The experimental results could provide a scientific basis for further development and utilization of mango leaf resources.展开更多
文摘The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.
文摘Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.
基金National Natural Science Foundation of China(81060336)Guangxi Natural Science Foundation(2011GXNSFF018006)。
文摘[Objectives]To determine the content of mangiferin and homomangiferin in mango leaves by HPLC.[Methods]The mangiferin and homomangiferin were separated and determined by Elite Hypersil C18(5μm,4.6 mm ID×250 mm)chromatographic column.Acetonitrile-0.1%(V/V)phosphoric acid solution was used as the mobile phase for gradient elution,the flow rate was 1.0 mL/min,the detection wavelength was 258 nm,the column temperature was 30℃,and the injection volume was 5μL.[Results]There was a good linear relationship between mangiferin and homomangiferin in the range of 0.0254-0.5080μg/μL(r=0.9999)and 0.000960-0.019200μg/μL(r=0.9999),respectively.The average recovery rate(n=6)of mangiferin and homomangiferin in mango leaves was 101.7%(RSD=2.0%)and 101.0%(RSD=1.7%),respectively.[Conclusions]There were great differences in the content of mangiferin and homomangiferin in the leaves of different varieties of mango.The experimental results could provide a scientific basis for further development and utilization of mango leaf resources.
