Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.

Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.

Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

In the actual complex environment,the recognition accuracy of crop leaf disease is often not high.Inspired by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition accuracy.TSPI-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal stream.PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf datasets.Furthermore,the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed.The experimental results show that as the number of interactions increases,the recognition accuracy of the network also increases.Finally,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

Performance of Deep Learning Techniques in Leaf Disease Detection

Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Diseases brought on by bacteria,viruses,and fungi are a significant factor in reduced crop yields.Numerous machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved accuracy.This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves.To effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and VGG-16.Our focus was on optimizing the hyper-parameters of these deep learningmodels with prior training.For the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.

Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.

PNN based crop disease recognition with leaf image features and meteorological data

An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological data.The images of infected crop leaves were taken under different environments of the growth periods,temperature and humidity.The methods of image morphological operation,contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image segmentation.From each image of infected crop leaf,the statistical features of color,texture and shape were extracted by image processing,and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction algorithm.The fusion feature vector of the image was formed by combining the statistical features and the meteorological features.Then the probabilistic neural networks(PNNs)classifier was adopted to evaluate the classification accuracy.The experimental results on three cucumber diseased leaf image datasets,i.e.,downy mildew,blight and anthracnose,showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology,the disease meteorological data and PNNs classifier,and the recognition accuracy rate was higher than 90%,which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.

Recognition System for Leaf Diseases of Ophiopogon japonicus Based on PCA-SVM

Taking leaf black spot,anthracnose and leaf blight of Ophiopogon japonicus as the research objects,lesions were separated by K-Means clustering segmentation technology.PCA(principal component analysis)was carried out on the 46-dimensional eigenvectors composed of color,shape and texture features,and then the multi-level classifier designed by SVM(support vector machine)was used to identify lesions.The recognition rate of the developed leaf disease recognition system of O.japonicus achieved 93.3%.The results indicates that the system is of great significance to the prevention and control of O.japonicus diseases and the modernization of O.japonicus industry.

Recognition of Ophiopogon japonicus Disease Based on Image Feature Fusion

The images of three diseases of black spot,anthracnose and leaf blight in Sichuan Ophiopogon japonicus leaves were taken the research object,and the bimodal method,Otsu threshold segmentation method and K-means clustering segmentation algorithm were compared and analyzed on the images of O.japonicus.The segmentation effect showed that the K-means clustering algorithm combined with the mathematical morphology processing method could meet the segmentation requirements.Then,the color,shape,and texture information of the lesion image were extracted and fused into a feature vector.Next,the variance analysis and principal component analysis method were applied to eliminate the feature parameters with poor disease characterization ability and reduce the eigenvector dimension to 10 dimensions.Finally,the classifiers for disease identification were designed by the support vector machine,and the recognition rate reached 90%after testing.The method has the advantages of low cost,simple algorithm and high efficiency,and basically meets the requirements of practical applications.

Estimating High-Order Functional Connectivity Networks for Mild Cognitive Impairment Identification Based on Topological Structure

Functional connectivity networks (FCNs) are important in the diagnosis of neurological diseases and the understanding of brain tissue patterns. Recently, many methods, such as Pearson’s correlation (PC), Sparse representation (SR), and Sparse low-rank representation have been proposed to estimate FCNs. Despite their popularity, they only capture the low-order connections of the brain regions, failing to encode more complex relationships (i.e. , high-order relationships). Although researchers have proposed high-order methods, like PC + PC and SR + SR, aiming to build FCNs that can reflect more real state of the brain. However, such methods only consider the relationships between brain regions during the FCN construction process, neglecting the potential shared topological structure information between FCNs of different subjects. In addition, the low-order relationships are always neglected during the construction of high-order FCNs. To address these issues, in this paper we proposed a novel method, namely Ho-FCNTops, towards estimating high-order FCNs based on brain topological structure. Specifically, inspired by the Group-constrained sparse representation (GSR), we first introduced a prior assumption that all subjects share the same topological structure in the construction of the low-order FCNs. Subsequently, we employed the Correlation-reserved embedding (COPE) to eliminate noise and redundancy from the low-order FCNs. Meanwhile, we retained the original low-order relationships during the embedding process to obtain new node representations. Finally, we utilized the SR method on the obtained new node representations to construct the Ho-FCNTops required for disease identification. To validate the effectiveness of the proposed method, experiments were conducted on 137 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to identify Mild Cognitive Impairment (MCI) patients from the normal controls. The experimental results demonstrate superior performance compared to baseline methods.

Crop pest image recognition based on the improved ViT method Author links open overlay panel

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.

Artificial intelligence can assist with diagnosing retinal vein occlusion

AIM:To assist with retinal vein occlusion(RVO)screening,artificial intelligence(AI)methods based on deep learning(DL)have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible.METHODS:A total of 8600 color fundus photographs(CFPs)were included for training,validation,and testing of disease recognition models and lesion segmentation models.Four disease recognition and four lesion segmentation models were established and compared.Finally,one disease recognition model and one lesion segmentation model were selected as superior.Additionally,224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models.RESULTS:Using the Inception-v3 model for disease identification,the mean sensitivity,specificity,and F1 for the three disease types and normal CFPs were 0.93,0.99,and 0.95,respectively,and the mean area under the curve(AUC)was 0.99.Using the DeepLab-v3 model for lesion segmentation,the mean sensitivity,specificity,and F1 for four lesion types(abnormally dilated and tortuous blood vessels,cotton-wool spots,flame-shaped hemorrhages,and hard exudates)were 0.74,0.97,and 0.83,respectively.CONCLUSION:DL models show good performance when recognizing RVO and identifying lesions using CFPs.Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists,DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.

A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks

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.

Apple leaf disease identification using genetic algorithm and correlation based feature selection method

Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.