Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits ...Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.展开更多
Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of t...Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.展开更多
Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artific...Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).展开更多
Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was...Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was built.The K-means clustering segmentation algorithm was used to segment the surface defects,and then color contrast was performed to determine whether it was a piece of defective fruit.Then,the shape features of normal fruit were extracted and an SVM classifier was designed to further determine its grade.This method has the advantages of low cost,simple algorithm and high efficiency,which opens a new way for fruit classification,and is of great significance to promoting the development of fruit classification industry in China and enhancing international competitiveness.展开更多
The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fre...The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.展开更多
Fruit is a key crop in worldwide agriculture feeding millions of people.The standard supply chain of fruit products involves quality checks to guarantee freshness,taste,and,most of all,safety.An important factor that ...Fruit is a key crop in worldwide agriculture feeding millions of people.The standard supply chain of fruit products involves quality checks to guarantee freshness,taste,and,most of all,safety.An important factor that determines fruit quality is its stage of ripening.This is usually manually classified by field experts,making it a labor-intensive and error-prone process.Thus,there is an arising need for automation in fruit ripeness classification.Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded.Machine learning and deep learning techniques dominate the top-performing methods.Furthermore,deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features,which are often crop-specific.In this survey,we review the latest methods proposed in the literature to automatize fruit ripeness classification,highlighting the most common feature descriptors they operate on.展开更多
The capability of Convolutional Neural Networks(CNNs)for sparse representation has significant application to complex tasks like Representation Learning(RL).However,labelled datasets of sufficient size for learning th...The capability of Convolutional Neural Networks(CNNs)for sparse representation has significant application to complex tasks like Representation Learning(RL).However,labelled datasets of sufficient size for learning this representation are not easily obtainable.The unsupervised learning capability of Variational Autoencoders(VAEs)and Generative Adversarial Networks(GANs)provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks.In this research,a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples.A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples.Two different VAE architectures are considered,a single layer dense VAE and a convolution based VAE,to compare the effectiveness of different architectures for learning of the representations.The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks.The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables。展开更多
Traditional manual visual grading of fruits has been one of the important challenges faced by the agricultural industry due to its laborious nature as well as inconsistency in inspec-tion and classification process.Au...Traditional manual visual grading of fruits has been one of the important challenges faced by the agricultural industry due to its laborious nature as well as inconsistency in inspec-tion and classification process.Automated defects detection using computer vision and machine learning has become a promising area of research with a high and direct impact on the domain of visual inspection.In this study,we propose an efficient and effective machine vision system based on the state-of-the-art deep learning techniques and stack-ing ensemble methods to offer a non-destructive and cost-effective solution for automat-ing the visual inspection of fruits’freshness and appearance.We trained,tested and compared the performance of various deep learning models including ResNet,DenseNet,MobileNetV2,NASNet and EfficientNet to find the best model for the grading of fruits.The proposed system also provides a real time visual inspection using a low cost Raspberry Pi module with a camera and a touchscreen display for user interaction.The real time sys-tem efficiently segments multiple instances of the fruits from an image and then grades the individual objects(fruits)accurately.The system was trained and tested on two data sets(apples and bananas)and the average accuracy was found to be 99.2% and 98.6% using EfficientNet model for apples and bananas test sets,respectively.Additionally,a slight improvement in the recognition rate(0.03% for apples and 0.06% for bananas)was noted while applying the stacking ensemble deep learning methods.The performance of the developed system has been found higher than the existing methods applied to the same data sets previously.Further,during real-time testing on actual samples,the accuracy was found to be 96.7% for apples and 93.8% for bananas which indicates the efficacy of the developed system.展开更多
Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identifica...Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03038540)by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries (IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs (MAFRA) (322063-03-1-SB010)by the Technology development Program (RS-2022-00156456)funded by the Ministry of SMEs and Startups (MSS,Korea).
文摘Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.
文摘Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.
基金the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2020-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).
基金Supported by the Chinese Society of Logistics(2021CSLKT3-286)。
文摘Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was built.The K-means clustering segmentation algorithm was used to segment the surface defects,and then color contrast was performed to determine whether it was a piece of defective fruit.Then,the shape features of normal fruit were extracted and an SVM classifier was designed to further determine its grade.This method has the advantages of low cost,simple algorithm and high efficiency,which opens a new way for fruit classification,and is of great significance to promoting the development of fruit classification industry in China and enhancing international competitiveness.
文摘The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.
文摘Fruit is a key crop in worldwide agriculture feeding millions of people.The standard supply chain of fruit products involves quality checks to guarantee freshness,taste,and,most of all,safety.An important factor that determines fruit quality is its stage of ripening.This is usually manually classified by field experts,making it a labor-intensive and error-prone process.Thus,there is an arising need for automation in fruit ripeness classification.Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded.Machine learning and deep learning techniques dominate the top-performing methods.Furthermore,deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features,which are often crop-specific.In this survey,we review the latest methods proposed in the literature to automatize fruit ripeness classification,highlighting the most common feature descriptors they operate on.
基金Edith Cowan University(ECU),Australia and Higher Education Commission(HEC)Pakistan,The Islamia University of Bahawalpur(IUB)Pakistan(5-1/HRD/UE STPI(Batch-V)/1182/2017/HEC).
文摘The capability of Convolutional Neural Networks(CNNs)for sparse representation has significant application to complex tasks like Representation Learning(RL).However,labelled datasets of sufficient size for learning this representation are not easily obtainable.The unsupervised learning capability of Variational Autoencoders(VAEs)and Generative Adversarial Networks(GANs)provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks.In this research,a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples.A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples.Two different VAE architectures are considered,a single layer dense VAE and a convolution based VAE,to compare the effectiveness of different architectures for learning of the representations.The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks.The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables。
文摘Traditional manual visual grading of fruits has been one of the important challenges faced by the agricultural industry due to its laborious nature as well as inconsistency in inspec-tion and classification process.Automated defects detection using computer vision and machine learning has become a promising area of research with a high and direct impact on the domain of visual inspection.In this study,we propose an efficient and effective machine vision system based on the state-of-the-art deep learning techniques and stack-ing ensemble methods to offer a non-destructive and cost-effective solution for automat-ing the visual inspection of fruits’freshness and appearance.We trained,tested and compared the performance of various deep learning models including ResNet,DenseNet,MobileNetV2,NASNet and EfficientNet to find the best model for the grading of fruits.The proposed system also provides a real time visual inspection using a low cost Raspberry Pi module with a camera and a touchscreen display for user interaction.The real time sys-tem efficiently segments multiple instances of the fruits from an image and then grades the individual objects(fruits)accurately.The system was trained and tested on two data sets(apples and bananas)and the average accuracy was found to be 99.2% and 98.6% using EfficientNet model for apples and bananas test sets,respectively.Additionally,a slight improvement in the recognition rate(0.03% for apples and 0.06% for bananas)was noted while applying the stacking ensemble deep learning methods.The performance of the developed system has been found higher than the existing methods applied to the same data sets previously.Further,during real-time testing on actual samples,the accuracy was found to be 96.7% for apples and 93.8% for bananas which indicates the efficacy of the developed system.
基金This work was partly supported by the Spanish MINECO,as well as European Commission FEDER funds,under grant TIN2015-66972-C5-3-R.
文摘Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.
