Plants employ multifaceted mechanisms to fight with numerous pathogens in nature. Resistance （R） genes are the most effective weapons against pathogen invasion since they can specifically recognize the corresponding...Plants employ multifaceted mechanisms to fight with numerous pathogens in nature. Resistance （R） genes are the most effective weapons against pathogen invasion since they can specifically recognize the corresponding pathogen effectors or associated protein（s） to activate plant immune responses at the site of infection. Up to date, over 70 R genes have been isolated from various plant species. Most R proteins contain conserved motifs such as nucleotide-binding site （NBS）, leucine-rich repeat （LRR）, Toll-interleukin-1 receptor domain （TIR, homologous to cytoplasmic domains of the Drosophila Toll protein and the manamalian intefleukin-1 receptor）, coiled-coil （CC） or leucine zipper （LZ） structure and protein kinase domain （PK）. Recent results indicate that these domains play significant roles in R protein interactions with effector proteins from pathogens and in activating signal transduction pathways involved in innate immunity. This review highlights an overview of the recent progress in elucidating the structure, function and evolution of the isolated R genes in different plant-pathogen interaction systems.展开更多
Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature ...Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature review of research since the discovery of rice false smut.We provide a comprehensive and,at times,critical overview of the main results and findings from related research,and propose future research directions.Firstly,we delve into the interaction between U.virens and rice,including the regulation of transcription factors,the process of U.virens infecting rice panicles,and the plant immune response caused by rice infection.Following that,we discuss the identification and characterization of mycotoxins produced by the pathogenic fungus,as well as strategies for disease management.We emphasize the importance of comprehensive agricultural prevention and control methods for the sustainable management of U.virens.This knowledge will update our understanding of the interaction between U.virens and rice plants,offering a valuable perspective for those interested in U.virens.展开更多
The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly.Genomic selection offers a potential solution to improve efficiency,but accurately predicting plant disease...The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly.Genomic selection offers a potential solution to improve efficiency,but accurately predicting plant disease resistance remains a challenge.In this study,we evaluated eight different machine learning(ML)methods,including random forest classification(RFC),support vector classifier(SVC),light gradient boosting machine(lightGBM),random forest classification plus kinship(RFC_K),support vector classification plus kinship(SVC_K),light gradient boosting machine plus kinship(lightGBM_K),deep neural network genomic prediction(DNNGP),and densely connected convolutional networks(DenseNet),for predicting plant disease resistance.Our results demonstrate that the three plus kinship(K)methods developed in this study achieved high prediction accuracy.Specifically,these methods achieved accuracies of up to 95%for rice blast(RB),85%for rice black-streaked dwarf virus(RBSDV),and 85%for rice sheath blight(RSB)when trained and applied to the rice diversity panel I(RDPI).Furthermore,the plus K models performed well in predicting wheat blast(WB)and wheat stripe rust(WSR)diseases,with mean accuracies of up to 90%and 93%,respectively.To assess the generalizability of our models,we applied the trained plus K methods to predict RB disease resistance in an independent population,rice diversity panel II(RDPII).Concurrently,we evaluated the RB resistance of RDPII cultivars using spray inoculation.Comparing the predictions with the spray inoculation results,we found that the accuracy of the plus K methods reached 91%.These findings highlight the effectiveness of the plus K methods(RFC_K,SVC_K,and lightGBM_K)in accurately predicting plant disease resistance for RB,RBSDV,RSB,WB,and WSR.The methods developed in this study not only provide valuable strategies for predicting disease resistance,but also pave the way for using machine learning to streamline genome-based crop breeding.展开更多
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.Dis...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%.展开更多
Blueberry,kiwifruit,Rosa roxburghii,and raspberry are the characteristic fruits planted in Guizhou Province.However,in recent years,harmful factors such as plant diseases and insect pests,pesticides and heavy metal re...Blueberry,kiwifruit,Rosa roxburghii,and raspberry are the characteristic fruits planted in Guizhou Province.However,in recent years,harmful factors such as plant diseases and insect pests,pesticides and heavy metal residues have affected the quality and safety of blueberry,kiwifruit,R.roxburghii,raspberry and other berries.These problems mainly include the frequent occurrence of plant diseases and insect pests,pesticide residues and heavy metal pollution,which not only seriously affect the quality and safety of berries,but also restrict the healthy development of berry industry.Therefore,it is very important to study the detection and monitoring of key hazard factors affecting the quality and safety of blueberry,kiwifruit,R.roxburghii and raspberry,as well as the standardized production technology.Using literature analysis,field investigation,questionnaire survey,comprehensive analysis,SWOT analysis,laboratory testing and other methods,this paper made a comprehensive and in-depth study of the berry industry in Guizhou Province.Through the analysis of the current situation of the berry industry in Guizhou Province,the problems and shortcomings in the planting,management,sales and other aspects of the industry were revealed.In order to solve these problems,a series of practical measures were put forward,including strengthening pest control,optimizing pesticide application technology,and strictly controlling heavy metal pollution,so as to ensure the healthy and stable development of berry industry.The implementation of these measures will help to improve the overall quality level of the berry industry in Guizhou Province.展开更多
Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.Wit...Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.With the development of artificial intelligence and computer vision,automatic recognition of plant diseases using image features has become feasible.As the support vector machine(SVM)is suitable for high dimension,high noise,and small sample learning,this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants.An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty(C-DCGAN-GP)was used to expand the segmentation of tea plant spots.Finally,the Visual Geometry Group 16(VGG16)deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.展开更多
Crop diseases have a significant impact on plant growth and can lead to reduced yields.Traditional methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent o...Crop diseases have a significant impact on plant growth and can lead to reduced yields.Traditional methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and knowledge.To address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification.In this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf diseases.The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases.The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes.Through rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural environments.The significance of this research lies in its potential to revolutionize plant disease detection and management practices.By automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise.The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.展开更多
The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information...The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.展开更多
Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and othe...Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and other parameters cause these diseases.In this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy.Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval.Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images.It is an efficient andflexible way of retrieving Tea Leaf images.It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP(mean average precision)scores,which is used as a parameter to judge the efficiency of the model.The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor.This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set.The proposed model will examine the input tea leaf image and identify the type of tea leaf disease.The relevant image will be retrieved based on the resulting type of disease.This model is only trained on scarce data as a real-life scenario,making it practical for many applications.展开更多
In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal d...In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.展开更多
A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure ...A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.展开更多
Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are prese...Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.展开更多
Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food security.Detecting these diseases early can help mitigate their impact and ensure he...Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food security.Detecting these diseases early can help mitigate their impact and ensure healthy crop yields.Machine learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected crops.These algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant dis-eases.Besides early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many more.Over the years,research has focused on using machine-learning algorithms for plant disease detection.Nevertheless,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease management.In view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and potato.We conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the years.After filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered relevant.From these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected plants.Results from our re-view show that deep learning and other algorithms performed significantly well in detecting plant diseases.In addition,we found a few references to plant disease management covering prevention,diagnosis,control,and monitoring.In view of this,little or no work has explored the prediction of the recovery of affected plants.Hence,we propose opportunities for developing machine learning-based technologies to cover prevention,diag-nosis,control,monitoring,and recovery.展开更多
By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant ...By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.展开更多
Aphids are phloem-feeding insects that reduce crop productivity due to feeding and transmission of plant viruses.When aphids disperse across the landscape to colonize new host plants,they will often probe on a wide va...Aphids are phloem-feeding insects that reduce crop productivity due to feeding and transmission of plant viruses.When aphids disperse across the landscape to colonize new host plants,they will often probe on a wide variety of nonhost plants before settling on a host suitable for feeding and reproduction.There is limited understanding of the diversity of plants that aphids probe on within a landscape,and characterizing this diversity can help us better understand host use patterns of aphids.Here,we used gut content analysis(GCA)to identify plant genera that were probed by aphid vectors of potato virus Y(PVY).Aphids were trapped weekly near potato fields during the growing seasons of 2020 and 2021 in San Luis Valley in Colorado.High-throughput sequencing of plant barcoding genes,trnF and ITS2,from 200 individual alate(i.e.,winged)aphids representing nine vector species of PVY was performed using the PacBio sequencing platform,and sequences were identi-fied to genus using NCBI BLASTn.We found that 34.7%of aphids probed upon presumed PVY host plants and that two of the most frequently detected plant genera,Solanum and Brassica,represent important crops and weeds within the study region.We found that 75%of aphids frequently probed upon PVY nonhosts including many species that are outside of their reported host ranges.Additionally,19%of aphids probed upon more than one plant species.This study provides the first evidence from high-throughput molecular GCA of aphids and reveals host use patterns that are relevant for PVY epidemiology.展开更多
[Objective] The antifungal bacteria of plant wilt disease was screened and identified to provide foundation for the study on bio-control preparation of plant wilt disease.[Method] Confrontation culture method was adop...[Objective] The antifungal bacteria of plant wilt disease was screened and identified to provide foundation for the study on bio-control preparation of plant wilt disease.[Method] Confrontation culture method was adopted to screen the bio-control bacteria with good antifungal effect against plant wilt disease,Biolog bacteria automatic identification system and 16S rDNA sequence analysis method were selected to identify its taxonomic status,the biological safety of the strain towards cotton and mice was also determined.[Result] 12 bacterial strains were isolated from rhizosphere of cotton.Among those strains,5 isolates showed antifungal activity against F.decemcellulare Brick,F.oxysporum f.sp.Diathi,F.oxysporum f.sp.vasinfectum.The antifungal effect of KL-1 strain against three target strains of pathogen reached 69.09%,80.78% and 78.89% respectively.Identification results of Biolog bacteria automatic identification system and 16S rDNA sequence analysis method showed that KL-1strain was Bacillus amyloliquefaciens;primary determination results of biological safety also showed that the strain KL-1 was safe and non-toxic towards cotton and mice.[Conclusion] KL-1strain of B.amyloliquefaciens had antifungal effect against several pathogens of plant wilt diseases,which was safe and non-toxic towards cotton and mice,being the bio-control strain with research and development potential.展开更多
[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromat...[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.展开更多
Rare earth phosphate fertilizer (REPF) as base fertilizer (750 kg per hm2) was applied in the western area of China during the 'Tenth Five-Year Plan' , and the results show as follows: compared with calcium su...Rare earth phosphate fertilizer (REPF) as base fertilizer (750 kg per hm2) was applied in the western area of China during the 'Tenth Five-Year Plan' , and the results show as follows: compared with calcium superphosphate (CK), REPF increases crops yields for maize by 17.0% , for rice by 10.5% , for wheat by 24.2% , for potato by 18.5% , for cabbage by 16.3%, for Chinese cabbage by 16.4%, for beet by 6.5%; decreases the diseased plant rate for common smut of maize by 1.0%, for maize stalk rot by 1.2%, for wheat take-all disease by 7.8%, for wheat root rot by 3.2%, for potato blackleg disease by 1.4%, for potato late blight by 6.6%; increases the sugar content of beet by 0.9°S.展开更多
Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; （ii） reducing p...Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; （ii） reducing production potential in agriculture due to competition for land in fertile areas and exhaustion of marginal arable lands; （iii） deteriorating ecology of agro-ecosystems and depletion of natural resources; and （iv） increased risk of disease epidemics resulting from agricultural intensification and monocultures. Future plant disease management should aim to strengthen food security for a stable society while simultaneously safeguarding the health of associated ecosystems and reducing dependency on natural resources. To achieve these multiple functionalities, sustainable plant disease management should place emphases on rational adaptation of resistance, avoidance, elimination and remediation strategies individually and collectively, guided by traits of specific host-pathogen associations using evolutionary ecology principles to create environmental （biotic and abiotic） conditions favorable for host growth and development while adverse to pathogen reproduction and evolution.展开更多
Agricultural application of rare earth (RE) has been generalized for several decades, and it is involved in crops, vegetables and stock raising in China. However, all the researches on RE mainly focus on the fields su...Agricultural application of rare earth (RE) has been generalized for several decades, and it is involved in crops, vegetables and stock raising in China. However, all the researches on RE mainly focus on the fields such as plant physiological activity, physiological and biochemical mechanism, sanitation toxicology and environmental security. Plant protection by using RE and the induced resistance of plant against diseases were summarized. The mechanism of rare earth against plant disease is highlighted, which includes following two aspects. First, RE elements can control some phytopathogen directly and reduce its virulence to host plant. Another possibility is that RE elements can affect host plant and induce the plant to produce some resistance to disease.展开更多
基金This work was supported by grants from the Natural Science Foundation of China (No. 30470990, No. 30571063)the"948"Project from the Minister of Agriculture in China, the"973"Project from the Minister of Science and Technology (No.2006CB101904)+1 种基金Hunan Natural Science Foundation (No.06JJ10006)Scientific Research Fund of Hunan Provincial Education department (No.04A024).
文摘Plants employ multifaceted mechanisms to fight with numerous pathogens in nature. Resistance （R） genes are the most effective weapons against pathogen invasion since they can specifically recognize the corresponding pathogen effectors or associated protein（s） to activate plant immune responses at the site of infection. Up to date, over 70 R genes have been isolated from various plant species. Most R proteins contain conserved motifs such as nucleotide-binding site （NBS）, leucine-rich repeat （LRR）, Toll-interleukin-1 receptor domain （TIR, homologous to cytoplasmic domains of the Drosophila Toll protein and the manamalian intefleukin-1 receptor）, coiled-coil （CC） or leucine zipper （LZ） structure and protein kinase domain （PK）. Recent results indicate that these domains play significant roles in R protein interactions with effector proteins from pathogens and in activating signal transduction pathways involved in innate immunity. This review highlights an overview of the recent progress in elucidating the structure, function and evolution of the isolated R genes in different plant-pathogen interaction systems.
基金supported by‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang Province,China(Grant No.2023C02014)Zhejiang Provincial Natural Science Foundation of China(Grant No.LY24C030002)+2 种基金Central Public-Interest Scientific Institution Basal Research Fund for China National Rice Research Institute(Grant No.CPSIBRF-CNRRI-202303)the China Agriculture Research System(Grant No.CARS-01)the Agricultural Science and Technology Innovation Program,China(Grant No.ASTIP)。
文摘Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature review of research since the discovery of rice false smut.We provide a comprehensive and,at times,critical overview of the main results and findings from related research,and propose future research directions.Firstly,we delve into the interaction between U.virens and rice,including the regulation of transcription factors,the process of U.virens infecting rice panicles,and the plant immune response caused by rice infection.Following that,we discuss the identification and characterization of mycotoxins produced by the pathogenic fungus,as well as strategies for disease management.We emphasize the importance of comprehensive agricultural prevention and control methods for the sustainable management of U.virens.This knowledge will update our understanding of the interaction between U.virens and rice plants,offering a valuable perspective for those interested in U.virens.
基金supported by the National Natural Science Foundation of China(32261143468)the National Key Research and Development(R&D)Program of China(2021YFC2600400)+1 种基金the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)001)the Project of Zhongshan Biological Breeding Laboratory(BM2022008-02)。
文摘The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly.Genomic selection offers a potential solution to improve efficiency,but accurately predicting plant disease resistance remains a challenge.In this study,we evaluated eight different machine learning(ML)methods,including random forest classification(RFC),support vector classifier(SVC),light gradient boosting machine(lightGBM),random forest classification plus kinship(RFC_K),support vector classification plus kinship(SVC_K),light gradient boosting machine plus kinship(lightGBM_K),deep neural network genomic prediction(DNNGP),and densely connected convolutional networks(DenseNet),for predicting plant disease resistance.Our results demonstrate that the three plus kinship(K)methods developed in this study achieved high prediction accuracy.Specifically,these methods achieved accuracies of up to 95%for rice blast(RB),85%for rice black-streaked dwarf virus(RBSDV),and 85%for rice sheath blight(RSB)when trained and applied to the rice diversity panel I(RDPI).Furthermore,the plus K models performed well in predicting wheat blast(WB)and wheat stripe rust(WSR)diseases,with mean accuracies of up to 90%and 93%,respectively.To assess the generalizability of our models,we applied the trained plus K methods to predict RB disease resistance in an independent population,rice diversity panel II(RDPII).Concurrently,we evaluated the RB resistance of RDPII cultivars using spray inoculation.Comparing the predictions with the spray inoculation results,we found that the accuracy of the plus K methods reached 91%.These findings highlight the effectiveness of the plus K methods(RFC_K,SVC_K,and lightGBM_K)in accurately predicting plant disease resistance for RB,RBSDV,RSB,WB,and WSR.The methods developed in this study not only provide valuable strategies for predicting disease resistance,but also pave the way for using machine learning to streamline genome-based crop breeding.
文摘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%.
基金Supported by Project of Science and Technology Development Center of the Ministry of Education of China(2022YFD1601704)Research Program of Huang Yanpei's Vocational Education Thought of China Vocational Education Association(ZJS2024YB181)+1 种基金Project of China Institute of Electronic Labor(Ceal2023269)New Generation Information Technology Innovation Project of High Education Institutions Scientific Research and Development Center of the Ministry of Education of China(2022IT120).
文摘Blueberry,kiwifruit,Rosa roxburghii,and raspberry are the characteristic fruits planted in Guizhou Province.However,in recent years,harmful factors such as plant diseases and insect pests,pesticides and heavy metal residues have affected the quality and safety of blueberry,kiwifruit,R.roxburghii,raspberry and other berries.These problems mainly include the frequent occurrence of plant diseases and insect pests,pesticide residues and heavy metal pollution,which not only seriously affect the quality and safety of berries,but also restrict the healthy development of berry industry.Therefore,it is very important to study the detection and monitoring of key hazard factors affecting the quality and safety of blueberry,kiwifruit,R.roxburghii and raspberry,as well as the standardized production technology.Using literature analysis,field investigation,questionnaire survey,comprehensive analysis,SWOT analysis,laboratory testing and other methods,this paper made a comprehensive and in-depth study of the berry industry in Guizhou Province.Through the analysis of the current situation of the berry industry in Guizhou Province,the problems and shortcomings in the planting,management,sales and other aspects of the industry were revealed.In order to solve these problems,a series of practical measures were put forward,including strengthening pest control,optimizing pesticide application technology,and strictly controlling heavy metal pollution,so as to ensure the healthy and stable development of berry industry.The implementation of these measures will help to improve the overall quality level of the berry industry in Guizhou Province.
基金Science and Technology Project of Jiangsu Polytechnic of Agriculture and Forestry(Project No.2021kj56)。
文摘Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.With the development of artificial intelligence and computer vision,automatic recognition of plant diseases using image features has become feasible.As the support vector machine(SVM)is suitable for high dimension,high noise,and small sample learning,this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants.An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty(C-DCGAN-GP)was used to expand the segmentation of tea plant spots.Finally,the Visual Geometry Group 16(VGG16)deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.
基金supported by the Hainan Provincial Natural Science Foundation of China(No.123QN182)Hainan University Research Fund(Project Nos.KYQD(ZR)-22064,KYQD(ZR)-22063,and KYQD(ZR)-22065).
文摘Crop diseases have a significant impact on plant growth and can lead to reduced yields.Traditional methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and knowledge.To address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification.In this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf diseases.The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases.The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes.Through rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural environments.The significance of this research lies in its potential to revolutionize plant disease detection and management practices.By automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise.The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.
基金funded by the University of Haripur,KP Pakistan Researchers Supporting Project number (PKURFL2324L33)。
文摘The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.
文摘Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and other parameters cause these diseases.In this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy.Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval.Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images.It is an efficient andflexible way of retrieving Tea Leaf images.It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP(mean average precision)scores,which is used as a parameter to judge the efficiency of the model.The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor.This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set.The proposed model will examine the input tea leaf image and identify the type of tea leaf disease.The relevant image will be retrieved based on the resulting type of disease.This model is only trained on scarce data as a real-life scenario,making it practical for many applications.
基金supported by the Guangxi Key R&D Project(Gui Ke AB21076021)the Project of Humanities and social sciences of“cultivation plan for thousands of young and middle-aged backbone teachers in Guangxi Colleges and universities”in 2021:Research on Collaborative integration of logistics service supply chain under high-quality development goals(2021QGRW044).
文摘In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.
基金This work was financially supported by MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-RS-2022-00156354)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program(Project No.P0016038).
文摘A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.
文摘Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.
文摘Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food security.Detecting these diseases early can help mitigate their impact and ensure healthy crop yields.Machine learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected crops.These algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant dis-eases.Besides early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many more.Over the years,research has focused on using machine-learning algorithms for plant disease detection.Nevertheless,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease management.In view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and potato.We conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the years.After filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered relevant.From these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected plants.Results from our re-view show that deep learning and other algorithms performed significantly well in detecting plant diseases.In addition,we found a few references to plant disease management covering prevention,diagnosis,control,and monitoring.In view of this,little or no work has explored the prediction of the recovery of affected plants.Hence,we propose opportunities for developing machine learning-based technologies to cover prevention,diag-nosis,control,monitoring,and recovery.
基金Supported by a Grant from the Science and Technology Project ofYunnan Province(2006NG02)~~
文摘By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.
文摘Aphids are phloem-feeding insects that reduce crop productivity due to feeding and transmission of plant viruses.When aphids disperse across the landscape to colonize new host plants,they will often probe on a wide variety of nonhost plants before settling on a host suitable for feeding and reproduction.There is limited understanding of the diversity of plants that aphids probe on within a landscape,and characterizing this diversity can help us better understand host use patterns of aphids.Here,we used gut content analysis(GCA)to identify plant genera that were probed by aphid vectors of potato virus Y(PVY).Aphids were trapped weekly near potato fields during the growing seasons of 2020 and 2021 in San Luis Valley in Colorado.High-throughput sequencing of plant barcoding genes,trnF and ITS2,from 200 individual alate(i.e.,winged)aphids representing nine vector species of PVY was performed using the PacBio sequencing platform,and sequences were identi-fied to genus using NCBI BLASTn.We found that 34.7%of aphids probed upon presumed PVY host plants and that two of the most frequently detected plant genera,Solanum and Brassica,represent important crops and weeds within the study region.We found that 75%of aphids frequently probed upon PVY nonhosts including many species that are outside of their reported host ranges.Additionally,19%of aphids probed upon more than one plant species.This study provides the first evidence from high-throughput molecular GCA of aphids and reveals host use patterns that are relevant for PVY epidemiology.
基金Supported by Natural Science Research Project in Universities in Jiangsu Province(10KJD210004)"Blue Project" Excellent Young Teacher Training Project in Universities in Jiangsu Province~~
文摘[Objective] The antifungal bacteria of plant wilt disease was screened and identified to provide foundation for the study on bio-control preparation of plant wilt disease.[Method] Confrontation culture method was adopted to screen the bio-control bacteria with good antifungal effect against plant wilt disease,Biolog bacteria automatic identification system and 16S rDNA sequence analysis method were selected to identify its taxonomic status,the biological safety of the strain towards cotton and mice was also determined.[Result] 12 bacterial strains were isolated from rhizosphere of cotton.Among those strains,5 isolates showed antifungal activity against F.decemcellulare Brick,F.oxysporum f.sp.Diathi,F.oxysporum f.sp.vasinfectum.The antifungal effect of KL-1 strain against three target strains of pathogen reached 69.09%,80.78% and 78.89% respectively.Identification results of Biolog bacteria automatic identification system and 16S rDNA sequence analysis method showed that KL-1strain was Bacillus amyloliquefaciens;primary determination results of biological safety also showed that the strain KL-1 was safe and non-toxic towards cotton and mice.[Conclusion] KL-1strain of B.amyloliquefaciens had antifungal effect against several pathogens of plant wilt diseases,which was safe and non-toxic towards cotton and mice,being the bio-control strain with research and development potential.
基金Supported by Natural Science Foundation in Education Department of Henan Province(2008B210001)~~
文摘[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.
基金Project supported by the National Important Tackling Researoh Foundation of China (2002BA315A-7)
文摘Rare earth phosphate fertilizer (REPF) as base fertilizer (750 kg per hm2) was applied in the western area of China during the 'Tenth Five-Year Plan' , and the results show as follows: compared with calcium superphosphate (CK), REPF increases crops yields for maize by 17.0% , for rice by 10.5% , for wheat by 24.2% , for potato by 18.5% , for cabbage by 16.3%, for Chinese cabbage by 16.4%, for beet by 6.5%; decreases the diseased plant rate for common smut of maize by 1.0%, for maize stalk rot by 1.2%, for wheat take-all disease by 7.8%, for wheat root rot by 3.2%, for potato blackleg disease by 1.4%, for potato late blight by 6.6%; increases the sugar content of beet by 0.9°S.
基金supported by the Fujian Technology Plan Project, China (2012N4001)the National Natural Science Foundation of China (U1405213)the Ministry of Science and Technology of National 973 Program of China (2014CB160315)
文摘Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; （ii） reducing production potential in agriculture due to competition for land in fertile areas and exhaustion of marginal arable lands; （iii） deteriorating ecology of agro-ecosystems and depletion of natural resources; and （iv） increased risk of disease epidemics resulting from agricultural intensification and monocultures. Future plant disease management should aim to strengthen food security for a stable society while simultaneously safeguarding the health of associated ecosystems and reducing dependency on natural resources. To achieve these multiple functionalities, sustainable plant disease management should place emphases on rational adaptation of resistance, avoidance, elimination and remediation strategies individually and collectively, guided by traits of specific host-pathogen associations using evolutionary ecology principles to create environmental （biotic and abiotic） conditions favorable for host growth and development while adverse to pathogen reproduction and evolution.
文摘Agricultural application of rare earth (RE) has been generalized for several decades, and it is involved in crops, vegetables and stock raising in China. However, all the researches on RE mainly focus on the fields such as plant physiological activity, physiological and biochemical mechanism, sanitation toxicology and environmental security. Plant protection by using RE and the induced resistance of plant against diseases were summarized. The mechanism of rare earth against plant disease is highlighted, which includes following two aspects. First, RE elements can control some phytopathogen directly and reduce its virulence to host plant. Another possibility is that RE elements can affect host plant and induce the plant to produce some resistance to disease.
