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
Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf ...Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean.展开更多
Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases...Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases.However,current DL methods often require substantial computational resources,hindering their application on resource-constrained devices.We propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this.The Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification.The proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet approach.More specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato diseases.The model could be used on mobile platforms because it is lightweight and designed with fewer layers.Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.展开更多
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
Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and qu...Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.展开更多
Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection appro...Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.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
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Supported by Heilongjiang Province Philosophy and Social Science Research Planning Project(17TQB059)。
文摘Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean.
基金thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/12/3)funded by Princess Nourah bint Abdulrahman University Researchers.Supporting Project Number(PNURSP2023R409),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases.However,current DL methods often require substantial computational resources,hindering their application on resource-constrained devices.We propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this.The Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification.The proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet approach.More specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato diseases.The model could be used on mobile platforms because it is lightweight and designed with fewer layers.Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.
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.
文摘Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.
文摘Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.
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%.
