Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only la...Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only labor-intensive and time-consuming but also subject to subjective biases and dependent on operators’ expertise. Recent advancements in Transformer-based architectures have shown substantial progress in image classification tasks, particularly excelling in global feature extraction. However, despite their strong performance, the high computational complexity and large parameter requirements of Transformer models limit their practical application in plant disease detection. To address these constraints, this study proposes an optimized Efficient Swin Transformer specifically engineered to reduce computational complexity while enhancing classification accuracy. This model is an improvement over the Swin-T architecture, incorporating two pivotal modules: the Selective Token Generator and the Feature Fusion Aggregator. The Selective Token Generator minimizes the number of tokens processed, significantly increasing computational efficiency and facilitating multi-scale feature extraction. Concurrently, the Feature Fusion Aggregator adaptively integrates static and dynamic features, thereby enhancing the model’s ability to capture complex details within intricate environmental contexts.Empirical evaluations conducted on the PlantDoc dataset demonstrate the model’s superior classification performance, achieving a precision of 80.14% and a recall of 76.27%. Compared to the standard Swin-T model, the Efficient Swin Transformer achieves approximately 20.89% reduction in parameter size while improving precision by 4.29%. This study substantiates the potential of efficient token conversion techniques within Transformer architectures, presenting an effective and accurate solution for plant disease detection in the agricultural sector.展开更多
Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neur...Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neural Networks(ANN).Unlike conventional ANNs,which process static images without fully capturing the inherent temporal dynamics,our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification,integrating an encoding method to convert static RGB plant images into temporally encoded spike trains.Additionally,while Bernoulli trials and standard deep learning architectures likeConvolutionalNeuralNetworks(CNNs)and Fully Connected Neural Networks(FCNNs)have been used extensively,our work is the first to integrate these trials within an SNN framework specifically for agricultural applications.This integration not only refines spike regulation and reduces computational overhead by 30%but also delivers superior accuracy(93.4%)in plant disease classification,marking a significant advancement in precision agriculture that has not been previously explored.Our approach uniquely transforms static plant leaf images into time-dependent representations,leveraging SNNs’intrinsic temporal processing capabilities.This approach aligns with the inherent ability of SNNs to capture dynamic,timedependent patterns,making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities.Unlike prior works,our hybrid encoding scheme dynamically adapts to pixel intensity variations(via threshold),enabling robust feature extraction under diverse agricultural conditions.The dual-stage preprocessing customizes the SNN’s behavior in two ways:the encoding threshold is derived from pixel distributions in diseased regions,and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices.We used a comprehensive dataset of 87,000 RGB images of plant leaves,which included 38 distinct classes of healthy and unhealthy leaves.To train and evaluate three distinct neural network architectures,DeepSNN,SimpleCNN,and SimpleFCNN,the dataset was rigorously preprocessed,including stochastic rotation,horizontal flip,resizing,and normalization.Moreover,by integrating Bernoulli trials to regulate spike generation,ourmethod focuses on extracting themost relevant featureswhile reducingcomputational overhead.Using a comprehensivedatasetof87,000RGB images across 38 classes,we rigorously preprocessed the data and evaluated three architectures:DeepSNN,SimpleCNN,and SimpleFCNN.The results demonstrate that DeepSNN outperforms the other models,achieving superior accuracy,efficient feature extraction,and robust spike management,thereby establishing the potential of SNNs for real-time,energy-efficient agricultural applications.展开更多
Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultura...Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultural Plant Bio-eng ineering Institute(Subtropical Fruit Research Institute).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
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
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
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
Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technolog...Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technology to improve productivity.Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity.Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s opinion.Deep learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant diseases.In this paper,the CNN model is proposed for the classification of rice and potato plant leaf diseases.Rice leaves are diagnosed with bacterial blight,blast,brown spot and tungro diseases.Potato leaf images are classified into three classes:healthy leaves,early blight and late blight diseases.Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study.The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%accuracy.The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.展开更多
Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with t...Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with the plants.These bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth promotion.In addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral pathogens.The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic,siderophore,and hydrolytic enzymes,competition for space and nutrients,and quorum sensing whereas,indirect mechanisms include rhizomicrobiome regulation via.secretion of root exudates,phytostimulation through the release of phytohormones viz.,auxin,cytokinin,gibberellic acid,1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz.,phenylalanine ammonia lyase(PAL),peroxidase(PO),polyphenyloxidases(PPO),superoxide dismutase(SOD),chitinase andβ-glucanases.For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering.In addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.展开更多
Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that...Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that would identify and solve the problem. At present, we live in a world dominated by technology on a significant scale, major network coverage, high-end smart-phones, as long as there are great discoveries and improvements in AI. The combination of high-end smart-phones and computer vision via Deep Learning has made possible what can be defined as “smartphone-assisted disease diagnosis”. In the area of Deep Learning, multiple architecture models have been trained, some achieving performance reaching more than 99.53% [1]. In this study, we evaluate CNN’s architectures applying transfer learning and deep feature extraction. All the features obtained will also be classified by SVM and KNN. Our work is feasible by the use of the open source Plant Village Dataset. The result obtained shows that SVM is the best classifier for leaf’s diseases detection.展开更多
The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, re...The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.展开更多
Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield...Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.展开更多
In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseas...In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.展开更多
Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecologic...Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecological resilience.However,technologies available are often limited due to different interests between producers and society and lacks of proper understanding of economic thresholds and the complex interactions among ecology,productivity and profitability.A comprehensive synergy and conflict evaluation of economic,sociological and ecological effects with technologies,productions and evolutionary principles as main components should be used to guide sustainable disease management that aims to mitigate crop and economic losses in the short term while maintaining functional farm ecosystem in the long term.Consequently,there should be an increased emphasis on technology development,public education and information exchange among governments,researchers,producers and consumers to broaden the options for disease management in the future.展开更多
Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe prob...Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.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
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
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
In order to provide the technological support for further implementing measures of reducing chemical pesticide to control plant diseases,the research progress on non-chemical pesticide measures to control plant diseas...In order to provide the technological support for further implementing measures of reducing chemical pesticide to control plant diseases,the research progress on non-chemical pesticide measures to control plant diseases are reviewed from the aspects of agricultural control,botanical pesticide control and microbial pesticide control,and the development prospects are proposed,including accelerating innovative research on botani-cal pesticide control such as Chinese herb extracts,and screening microbial pesticides from valuable bio-control bacteria or plant endophyte metabolites for commercial production and utilization.展开更多
文摘Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only labor-intensive and time-consuming but also subject to subjective biases and dependent on operators’ expertise. Recent advancements in Transformer-based architectures have shown substantial progress in image classification tasks, particularly excelling in global feature extraction. However, despite their strong performance, the high computational complexity and large parameter requirements of Transformer models limit their practical application in plant disease detection. To address these constraints, this study proposes an optimized Efficient Swin Transformer specifically engineered to reduce computational complexity while enhancing classification accuracy. This model is an improvement over the Swin-T architecture, incorporating two pivotal modules: the Selective Token Generator and the Feature Fusion Aggregator. The Selective Token Generator minimizes the number of tokens processed, significantly increasing computational efficiency and facilitating multi-scale feature extraction. Concurrently, the Feature Fusion Aggregator adaptively integrates static and dynamic features, thereby enhancing the model’s ability to capture complex details within intricate environmental contexts.Empirical evaluations conducted on the PlantDoc dataset demonstrate the model’s superior classification performance, achieving a precision of 80.14% and a recall of 76.27%. Compared to the standard Swin-T model, the Efficient Swin Transformer achieves approximately 20.89% reduction in parameter size while improving precision by 4.29%. This study substantiates the potential of efficient token conversion techniques within Transformer architectures, presenting an effective and accurate solution for plant disease detection in the agricultural sector.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neural Networks(ANN).Unlike conventional ANNs,which process static images without fully capturing the inherent temporal dynamics,our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification,integrating an encoding method to convert static RGB plant images into temporally encoded spike trains.Additionally,while Bernoulli trials and standard deep learning architectures likeConvolutionalNeuralNetworks(CNNs)and Fully Connected Neural Networks(FCNNs)have been used extensively,our work is the first to integrate these trials within an SNN framework specifically for agricultural applications.This integration not only refines spike regulation and reduces computational overhead by 30%but also delivers superior accuracy(93.4%)in plant disease classification,marking a significant advancement in precision agriculture that has not been previously explored.Our approach uniquely transforms static plant leaf images into time-dependent representations,leveraging SNNs’intrinsic temporal processing capabilities.This approach aligns with the inherent ability of SNNs to capture dynamic,timedependent patterns,making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities.Unlike prior works,our hybrid encoding scheme dynamically adapts to pixel intensity variations(via threshold),enabling robust feature extraction under diverse agricultural conditions.The dual-stage preprocessing customizes the SNN’s behavior in two ways:the encoding threshold is derived from pixel distributions in diseased regions,and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices.We used a comprehensive dataset of 87,000 RGB images of plant leaves,which included 38 distinct classes of healthy and unhealthy leaves.To train and evaluate three distinct neural network architectures,DeepSNN,SimpleCNN,and SimpleFCNN,the dataset was rigorously preprocessed,including stochastic rotation,horizontal flip,resizing,and normalization.Moreover,by integrating Bernoulli trials to regulate spike generation,ourmethod focuses on extracting themost relevant featureswhile reducingcomputational overhead.Using a comprehensivedatasetof87,000RGB images across 38 classes,we rigorously preprocessed the data and evaluated three architectures:DeepSNN,SimpleCNN,and SimpleFCNN.The results demonstrate that DeepSNN outperforms the other models,achieving superior accuracy,efficient feature extraction,and robust spike management,thereby establishing the potential of SNNs for real-time,energy-efficient agricultural applications.
文摘Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultural Plant Bio-eng ineering Institute(Subtropical Fruit Research Institute).
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.
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 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.
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.
基金This research supported by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia under Grant Number KAU 2020/251.
文摘Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technology to improve productivity.Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity.Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s opinion.Deep learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant diseases.In this paper,the CNN model is proposed for the classification of rice and potato plant leaf diseases.Rice leaves are diagnosed with bacterial blight,blast,brown spot and tungro diseases.Potato leaf images are classified into three classes:healthy leaves,early blight and late blight diseases.Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study.The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%accuracy.The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.
文摘Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with the plants.These bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth promotion.In addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral pathogens.The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic,siderophore,and hydrolytic enzymes,competition for space and nutrients,and quorum sensing whereas,indirect mechanisms include rhizomicrobiome regulation via.secretion of root exudates,phytostimulation through the release of phytohormones viz.,auxin,cytokinin,gibberellic acid,1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz.,phenylalanine ammonia lyase(PAL),peroxidase(PO),polyphenyloxidases(PPO),superoxide dismutase(SOD),chitinase andβ-glucanases.For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering.In addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.
文摘Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that would identify and solve the problem. At present, we live in a world dominated by technology on a significant scale, major network coverage, high-end smart-phones, as long as there are great discoveries and improvements in AI. The combination of high-end smart-phones and computer vision via Deep Learning has made possible what can be defined as “smartphone-assisted disease diagnosis”. In the area of Deep Learning, multiple architecture models have been trained, some achieving performance reaching more than 99.53% [1]. In this study, we evaluate CNN’s architectures applying transfer learning and deep feature extraction. All the features obtained will also be classified by SVM and KNN. Our work is feasible by the use of the open source Plant Village Dataset. The result obtained shows that SVM is the best classifier for leaf’s diseases detection.
基金supported by the National Programs of Public-Beneficiary Sectors Funds,Ministryof Science and Technology,China(200803024)
文摘The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.
文摘Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.
文摘In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.
基金This work was supported by the National Natural Science Foundation of China(72073028)the Natural Science Foundation of Fujian Province of China(2018J01707).
文摘Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecological resilience.However,technologies available are often limited due to different interests between producers and society and lacks of proper understanding of economic thresholds and the complex interactions among ecology,productivity and profitability.A comprehensive synergy and conflict evaluation of economic,sociological and ecological effects with technologies,productions and evolutionary principles as main components should be used to guide sustainable disease management that aims to mitigate crop and economic losses in the short term while maintaining functional farm ecosystem in the long term.Consequently,there should be an increased emphasis on technology development,public education and information exchange among governments,researchers,producers and consumers to broaden the options for disease management in the future.
文摘Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.
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.
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.
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Supported by Open Fund Project of Key Laboratory of Plant Nutrition and Fertilizer,Ministry of Agriculture and Rural Affairs"Study on Precise Nutrient Demand Model of Corn"(KLPNF-2018-4)
文摘In order to provide the technological support for further implementing measures of reducing chemical pesticide to control plant diseases,the research progress on non-chemical pesticide measures to control plant diseases are reviewed from the aspects of agricultural control,botanical pesticide control and microbial pesticide control,and the development prospects are proposed,including accelerating innovative research on botani-cal pesticide control such as Chinese herb extracts,and screening microbial pesticides from valuable bio-control bacteria or plant endophyte metabolites for commercial production and utilization.
