Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from ima...Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.展开更多
Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artif...Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.展开更多
With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is...With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.展开更多
Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best tim...Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.展开更多
In order to provide a theoretical basis for apple cultivation techniques and to provide a reference for preparation of scientific management measures,the occurrence and damage status of apple little leaf in northern G...In order to provide a theoretical basis for apple cultivation techniques and to provide a reference for preparation of scientific management measures,the occurrence and damage status of apple little leaf in northern Guizhou was investigated,and fertilizer control test against the disease was also conducted.（1）Apple little leaf occurred from April to October in apple producing areas in northern Guizhou every year,with heavy incidence from April to May,light incidence from June to July,and the lightest incidence from August to October.（2） Effectiveness test showed that application of zinc sulfate fertilizer 135 g ＋ cow dung 10 kg received the best control effect,followed by zinc sulfate fertilizer 165 g ＋ cow dung 10 kg,then zinc sulfate fertilizer 105 g ＋ cow dung 10 kg and zinc sulfate fertilizer 150 g ＋ cow dung 10 kg.（3） Based on local environmental conditions,occurrence and damage situation,incidence conditions and fertilizer efficacy test,comprehensive prevention and control technology with rational supplement of zinc fertilizer could be used to control apple little leaf.展开更多
The demand for natural fibers has always been high due to their unique characteristics like strength, lightweight, availability, bio-degradability, etc. In every phase of life, from clothing to technical textiles, nat...The demand for natural fibers has always been high due to their unique characteristics like strength, lightweight, availability, bio-degradability, etc. In every phase of life, from clothing to technical textiles, natural fibers are used. Water absorption of fibers is considered really important in many aspects, e.g., Sportech, Medtech, Geotech, etc. This work analyses water absorption of raw and alkali-treated cotton, arecas, pineapple leaves, and banana fibers. Fibers were scoured with different concentrations of alkali (2, 4, 6 gm/L NaOH), washed and neutralized with the dilute acetic acid solution, then dried. Later on, the fiber samples were immersed into distilled water, and water absorption percentages of the fibers were determined every 10 minutes within 1 hour in total. It appeared that at untreated conditions, the areca fiber has the highest water absorption capacity compared to the other fibers. Alkali-treated cotton shows the highest water absorption, and areca fibers show approximately 60% water absorption of cotton.展开更多
Soil Plant Analysis Development(SPAD)Chlorophyll Meter reading was used to effectively characterize chlorophyll content,which is an important indicator of the health status of plant leaves.In this study,the hyperspect...Soil Plant Analysis Development(SPAD)Chlorophyll Meter reading was used to effectively characterize chlorophyll content,which is an important indicator of the health status of plant leaves.In this study,the hyperspectral images of apple leaves infected by apple mosaic virus(ApMV)were captured,and their SPAD values were measured.The spectral reflectance of leaves with varying degree infection of disease is significantly different.In particular,the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe infection.Several hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves(correlation coefficient>0.9).Models were established to estimate apple foliar SPAD values based on these vegetation indices.Among the models,the multivariate regression model with partial least square regression(PLSR)method achieved the highest accuracy.The SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple leaf.Furthermore,the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple model.It can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.展开更多
基金supported in part by the General Program Hunan Provincial Natural Science Foundation of 2022,China(2022JJ31022)the Undergraduate Education Reform Project of Hunan Province,China(HNJG-20210532)the National Natural Science Foundation of China(62276276)。
文摘Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP2/209/42),www.kku.e du.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.
文摘Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.
基金Natural Science Foundation of China(grant Nos.61473237,61202170,and 61402331)It is also supported by the Shaanxi Provincial Natural Science Foundation Research Project(2014JM2-6096)+3 种基金Tianjin Research Program of Application Foundation and Advanced Technology(14JCYBJC42500)Tianjin science and technology correspondent project(16JCTPJC47300)the 2015 key projects of Tianjin science and technology support program(No.15ZCZDGX00200)the Fund of Tianjin Food Safety&Low Carbon Manufacturing Collaborative Innovation Center.
文摘Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.
文摘In order to provide a theoretical basis for apple cultivation techniques and to provide a reference for preparation of scientific management measures,the occurrence and damage status of apple little leaf in northern Guizhou was investigated,and fertilizer control test against the disease was also conducted.（1）Apple little leaf occurred from April to October in apple producing areas in northern Guizhou every year,with heavy incidence from April to May,light incidence from June to July,and the lightest incidence from August to October.（2） Effectiveness test showed that application of zinc sulfate fertilizer 135 g ＋ cow dung 10 kg received the best control effect,followed by zinc sulfate fertilizer 165 g ＋ cow dung 10 kg,then zinc sulfate fertilizer 105 g ＋ cow dung 10 kg and zinc sulfate fertilizer 150 g ＋ cow dung 10 kg.（3） Based on local environmental conditions,occurrence and damage situation,incidence conditions and fertilizer efficacy test,comprehensive prevention and control technology with rational supplement of zinc fertilizer could be used to control apple little leaf.
文摘The demand for natural fibers has always been high due to their unique characteristics like strength, lightweight, availability, bio-degradability, etc. In every phase of life, from clothing to technical textiles, natural fibers are used. Water absorption of fibers is considered really important in many aspects, e.g., Sportech, Medtech, Geotech, etc. This work analyses water absorption of raw and alkali-treated cotton, arecas, pineapple leaves, and banana fibers. Fibers were scoured with different concentrations of alkali (2, 4, 6 gm/L NaOH), washed and neutralized with the dilute acetic acid solution, then dried. Later on, the fiber samples were immersed into distilled water, and water absorption percentages of the fibers were determined every 10 minutes within 1 hour in total. It appeared that at untreated conditions, the areca fiber has the highest water absorption capacity compared to the other fibers. Alkali-treated cotton shows the highest water absorption, and areca fibers show approximately 60% water absorption of cotton.
基金This work was supported by the National High Technology Research and Development Program of China(Grant No.2013AA102401)the Growth plan for young talents in Shanghai municipal agricultural system(Hu Nong Qing Zi(2018)No.1-29).
文摘Soil Plant Analysis Development(SPAD)Chlorophyll Meter reading was used to effectively characterize chlorophyll content,which is an important indicator of the health status of plant leaves.In this study,the hyperspectral images of apple leaves infected by apple mosaic virus(ApMV)were captured,and their SPAD values were measured.The spectral reflectance of leaves with varying degree infection of disease is significantly different.In particular,the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe infection.Several hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves(correlation coefficient>0.9).Models were established to estimate apple foliar SPAD values based on these vegetation indices.Among the models,the multivariate regression model with partial least square regression(PLSR)method achieved the highest accuracy.The SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple leaf.Furthermore,the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple model.It can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.
