Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many effor...Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many efforts are made to automate plant classification using plant leaf,plant flower,bark,or stem.After much effort,it has been proven that leaf is the most reliable source for plant classification.But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and venation.Therefore,it is required to normalize all plant leaves into the same size to get better performance.Convolutional Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this approach.But the performance can be improved by classifying using the traditional approach after applying CNN.In this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew Dataset.The datasets are augmented to take care all the possibilities.The assessments and correlations of the predetermined feature extractor models are given.CNN+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.展开更多
Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and s...Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.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
The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf bl...The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.展开更多
Agriculture is the basis of every economy worldwide.Crop production is one of the major factors affecting domestic market condition in any country.Agricultural production is also a major prerequisite of economic devel...Agriculture is the basis of every economy worldwide.Crop production is one of the major factors affecting domestic market condition in any country.Agricultural production is also a major prerequisite of economic development,be it any part of any country.It plays a crucial role as it even provides raw material,employment and food to different citizens.A lot of issues are responsible for estimated crop production varying in different parts of the world.Some of these include overutilization of chemical fertilizers,presence of chemicals in water supply,uneven distribution of rainfall,different soil fertility and others.Other than these issues one of the commonly faced challenges across the globe equally includes destruction of themajor part of production due to diseases.After providing effective resources to the fields,major section of the production is diminished by the presence of diseases in the plants grown.This leads to focus on effective ways of detection of disease in plants.Presence of various diseases in plant is a major concern among farmers.Plant diseases acts as a major threat to small scale farmers as they lead tomajor destruction in overall food supply.To provide effectivemeasures for detection and avoidance of the destruction requires an early identification of type of plant disease present.In recent timemajorwork is being done for the identification of plant disease presents in varied parts of theworld affection varied crops.Majorwork is being done in the domain of identification of causing factors of these diseases.Someof the diseases are marked by the presence of viruses while some are resultant of fungal infection.This becomes a major issuewhen the causing factor is not traceable before it has already spread to major production section.This paper brings a review on effective use of different imaging techniques and computer vision approaches for the identification and classification of plant diseases.Detection of Plant disease is initiated with image acquisition followed by pre-processingwhile using the process of segmentation.It is further accompanied by different techniques used for feature extraction alongwith classification.In this Paper we present the Current Trends and Challenges for detection of plant disease using computer vision and advance imaging technique.展开更多
In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot le...In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.展开更多
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/10)Taif University,Taif,Saudi Arabia.
文摘Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many efforts are made to automate plant classification using plant leaf,plant flower,bark,or stem.After much effort,it has been proven that leaf is the most reliable source for plant classification.But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and venation.Therefore,it is required to normalize all plant leaves into the same size to get better performance.Convolutional Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this approach.But the performance can be improved by classifying using the traditional approach after applying CNN.In this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew Dataset.The datasets are augmented to take care all the possibilities.The assessments and correlations of the predetermined feature extractor models are given.CNN+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.
基金This work was supported by the Deanship of Scientific Research,King Saud University,Saudi Arabia.
文摘Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.
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.
基金supported by Center for Engineering Research and Development,Government of Kerala,India,vide Grant No.KTU/Research/2743/2017.
文摘The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.
文摘Agriculture is the basis of every economy worldwide.Crop production is one of the major factors affecting domestic market condition in any country.Agricultural production is also a major prerequisite of economic development,be it any part of any country.It plays a crucial role as it even provides raw material,employment and food to different citizens.A lot of issues are responsible for estimated crop production varying in different parts of the world.Some of these include overutilization of chemical fertilizers,presence of chemicals in water supply,uneven distribution of rainfall,different soil fertility and others.Other than these issues one of the commonly faced challenges across the globe equally includes destruction of themajor part of production due to diseases.After providing effective resources to the fields,major section of the production is diminished by the presence of diseases in the plants grown.This leads to focus on effective ways of detection of disease in plants.Presence of various diseases in plant is a major concern among farmers.Plant diseases acts as a major threat to small scale farmers as they lead tomajor destruction in overall food supply.To provide effectivemeasures for detection and avoidance of the destruction requires an early identification of type of plant disease present.In recent timemajorwork is being done for the identification of plant disease presents in varied parts of theworld affection varied crops.Majorwork is being done in the domain of identification of causing factors of these diseases.Someof the diseases are marked by the presence of viruses while some are resultant of fungal infection.This becomes a major issuewhen the causing factor is not traceable before it has already spread to major production section.This paper brings a review on effective use of different imaging techniques and computer vision approaches for the identification and classification of plant diseases.Detection of Plant disease is initiated with image acquisition followed by pre-processingwhile using the process of segmentation.It is further accompanied by different techniques used for feature extraction alongwith classification.In this Paper we present the Current Trends and Challenges for detection of plant disease using computer vision and advance imaging technique.
基金R.A.Krohling thanks the Brazilian research agency Conselho Nacional de Desenvolvimento Científico e Tecnólogico(CNPq)for financial support under grant no.304688/2021-5.
文摘In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.
