Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this stud...Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.展开更多
In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectiv...In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectively and accurately segmenting leaf lesions presents considerable challenges.Existing grape leaf lesion segmentationmodels have several limitations,such as a large number of parameters,long training durations,and limited precision in extracting small lesions and boundary details.To address these issues,we propose an enhanced DeepLabv3+model incorporating Strip Pooling,Content-Guided Fusion,and Convolutional Block Attention Module(SFC_DeepLabv3+),an enhanced lesion segmentation method based on DeepLabv3+.This approach uses the lightweight MobileNetv2 backbone to replace the original Xception,incorporates a lightweight convolutional block attention module,and introduces a content-guided feature fusion module to improve the detection accuracy of small lesions and blurred boundaries.Experimental results showthat the enhancedmodel achieves a mean Intersection overUnion(mIoU)of 90.98%,amean Pixel Accuracy(mPA)of 94.33%,and a precision of 95.84%.This represents relative gains of 2.22%,1.78%,and 0.89%respectively compared to the original model.Additionally,its complexity is significantly reduced without sacrificing performance,the parameter count is reduced to 6.27 M,a decrease of 88.5%compared to the original model,floating point of operations(GFLOPs)drops from 83.62 to 29.00 G,a reduction of 65.1%.Additionally,Frames Per Second(FPS)increases from 63.7 to 74.3 FPS,marking an improvement of 16.7%.Compared to other models,the improved architecture shows faster convergence and superior segmentation accuracy,making it highly suitable for applications in resource-constrained environments.展开更多
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
There is a growing concern over the presence of high concentrations of nitrate in orally consumed herbal remedies. Since nitrate accumulation in the plant body can vary with spatial location in plant and plant develop...There is a growing concern over the presence of high concentrations of nitrate in orally consumed herbal remedies. Since nitrate accumulation in the plant body can vary with spatial location in plant and plant development and temporally with photoperiod, understanding the relations between these factors and nitrate contents in the consumed herbs are vital for the development of agrotechnical strategies for nitrate avoidance. Therefore, the distribution profiles of nitrate content and nitrate reductase （NR） activity were analyzed in 14 accessions of Andrographis paniculata （Burm.f.） Wall. ex Nees., a medicinal plant widely used as a component of many herbal teas and medicinal infusions. Significant variation in nitrate accumulation in the plant was observed between the accessions, and most contained nitrate levels beyond the safety limit recommended according to the European Commission＇s Scientific Committee on Food. Nitrate content increased with plant development and leaf age, and that in leaf segment was higher in the middle of the leaf than at its tip or base; the nitrate concentration in plant organ followed the order of petiole 〉 leaf 〉 stem, and it was lower at mid-day than in the early morning or evening. Gradients of NR activity were negatively correlated with nitrate content in all studied temporal and spatial profiles. Identification of the characteristic spatial and developmental profiles of nitrate accumulation and NR activity in A. paniculata can guide the development of harvest strategies, aiming at minimizing nitrate contents in the consumed herbal tissue.展开更多
Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease...Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .展开更多
To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,...To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.展开更多
Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the ...Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.展开更多
Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to ...Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.展开更多
基金This study was conducted within the project FraxVir“Detection,characterisation and analyses of the occurrence of viruses and ash dieback in special stands of Fraxinus excelsior-a supplementary study to the FraxForFuture demonstration project”and receives funding via the Waldklimafonds(WKF)funded by the German Federal Ministry of Food and Agriculture(BMEL)and Federal Ministry for the Environment,Nature Conservation,Nuclear Safety and Consumer Protection(BMUV)administrated by the Agency for Renewable Resources(FNR)under grant agreement 2220WK40A4.
文摘Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.
基金supported by the following grants:Zhejiang A&F University Research Development Fund(Talent Initiation Project No.2021LFR048)and 2023 University-Enterprise Joint Research Program(Grant No.LHYFZ2302)from the Modern Agricultural and Forestry Artificial Intelligence Industry Academy.
文摘In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectively and accurately segmenting leaf lesions presents considerable challenges.Existing grape leaf lesion segmentationmodels have several limitations,such as a large number of parameters,long training durations,and limited precision in extracting small lesions and boundary details.To address these issues,we propose an enhanced DeepLabv3+model incorporating Strip Pooling,Content-Guided Fusion,and Convolutional Block Attention Module(SFC_DeepLabv3+),an enhanced lesion segmentation method based on DeepLabv3+.This approach uses the lightweight MobileNetv2 backbone to replace the original Xception,incorporates a lightweight convolutional block attention module,and introduces a content-guided feature fusion module to improve the detection accuracy of small lesions and blurred boundaries.Experimental results showthat the enhancedmodel achieves a mean Intersection overUnion(mIoU)of 90.98%,amean Pixel Accuracy(mPA)of 94.33%,and a precision of 95.84%.This represents relative gains of 2.22%,1.78%,and 0.89%respectively compared to the original model.Additionally,its complexity is significantly reduced without sacrificing performance,the parameter count is reduced to 6.27 M,a decrease of 88.5%compared to the original model,floating point of operations(GFLOPs)drops from 83.62 to 29.00 G,a reduction of 65.1%.Additionally,Frames Per Second(FPS)increases from 63.7 to 74.3 FPS,marking an improvement of 16.7%.Compared to other models,the improved architecture shows faster convergence and superior segmentation accuracy,making it highly suitable for applications in resource-constrained environments.
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
文摘There is a growing concern over the presence of high concentrations of nitrate in orally consumed herbal remedies. Since nitrate accumulation in the plant body can vary with spatial location in plant and plant development and temporally with photoperiod, understanding the relations between these factors and nitrate contents in the consumed herbs are vital for the development of agrotechnical strategies for nitrate avoidance. Therefore, the distribution profiles of nitrate content and nitrate reductase （NR） activity were analyzed in 14 accessions of Andrographis paniculata （Burm.f.） Wall. ex Nees., a medicinal plant widely used as a component of many herbal teas and medicinal infusions. Significant variation in nitrate accumulation in the plant was observed between the accessions, and most contained nitrate levels beyond the safety limit recommended according to the European Commission＇s Scientific Committee on Food. Nitrate content increased with plant development and leaf age, and that in leaf segment was higher in the middle of the leaf than at its tip or base; the nitrate concentration in plant organ followed the order of petiole 〉 leaf 〉 stem, and it was lower at mid-day than in the early morning or evening. Gradients of NR activity were negatively correlated with nitrate content in all studied temporal and spatial profiles. Identification of the characteristic spatial and developmental profiles of nitrate accumulation and NR activity in A. paniculata can guide the development of harvest strategies, aiming at minimizing nitrate contents in the consumed herbal tissue.
基金supported by the Changsha Municipal Natural Science Foundation(grant no.kq2014160)in part by the National Natural Science Foundation in China(grant no.61703441)+2 种基金in part by the Key Projects of the Department of Education,Hunan Province(grant no.19A511)in part by the Hunan Key Laboratory of Intelligent Logistics Technology(grant no.2019TP1015)in part by the National Natural Science Foundation of China(grant no.61902436).
文摘Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .
基金This study was supported by the National Natural Science Foundation of China(No.61403035)Natural Science Foundation of Beijing Municipality(No.9152009)Science and Technology Innovation Ability Construction Project of Beijing Academy of Agriculture and Forestry Science(No.KJCX20170206).
文摘To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.
文摘Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.
文摘Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.
