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
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 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
Across all Russia global climate change is observed. Consequences of climatic changes, undoubtedly, will be reflected in distribution of harmful organisms, their injuriousness and will demand development of new approa...Across all Russia global climate change is observed. Consequences of climatic changes, undoubtedly, will be reflected in distribution of harmful organisms, their injuriousness and will demand development of new approaches in plant protection. Over the last 10 years, the spread of cereal crop diseases in the Northwest Russia has been monitored. The purpose of researches is to find new diseases in the Northwest region of Russia. Disease progression was mainly monitored 3 or 4 times during the growing season, from germination to crop maturity. As a result in this region the new diseases were found. In 2005-2007 the causal agent of yellow leaf spot Pyrenophora tritici-repentis was found on wheat. Fusarium graminearum historically has two areas in Russia： the North Caucasus and the Far East. However, since 2003 F. graminearum appeared on the territory of the North-West of Russia. Septoria tritici became the main pathogen of wheat in the North-Western Region.. In 2013 Ramularia collo-cygni was found in Arkhangelsk region. These observations suggest that global warming of climate leads to an expansion south species pathogen to the north regions of Russia.展开更多
The deeper the degree of urbanization, the higher the level of landscaping in this area. The degree of development of urban landscaping reflects the economic level and development of the city. With the country paying ...The deeper the degree of urbanization, the higher the level of landscaping in this area. The degree of development of urban landscaping reflects the economic level and development of the city. With the country paying more and more attention to the construction of ecological civilization, more and more cities have speeded up the development of landscaping. Landscape construction is not only beneficial to the construction of ecological civilization and the improvement of urban environment, but also adds a bit of beautiful scenery to cities. Under the background of ecological civilization construction, this paper makes some explorations on the related problems of prevention and control of plant diseases and insect pests in gardens, so as to better solve the problem of plant diseases and insect pests and promote the construction of ecological civilization in cities.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
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
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
The incidence of plant bacterial diseases has been rising annually alongside the development of facility agriculture,which can seriously affect the yield and quality of crops,and cause significant losses to agricultur...The incidence of plant bacterial diseases has been rising annually alongside the development of facility agriculture,which can seriously affect the yield and quality of crops,and cause significant losses to agricultural production globally.Thus,the development of highly effective prevention and control agents targeting plant pathogenic bacteria are urgently needed.The Type III secretion system(T3SS)is a complex molecular apparatus exist in many Gram-negative pathogenic bacteria,which plays a crucial role in the pathogenic process of bacteria.In recent years,T3SS has become an important target for the development of novel antibacterial agents.Here,we highlight the recent findings on a plant defense compound targeting T3SS for plant bacterial diseases by Lei and his colleagues.展开更多
Trichoderma strains possessing biological control functions have been used in agriculture against phytopathogens.Currently,only very few species of the genus were applied to or involved in plant disease control.Discov...Trichoderma strains possessing biological control functions have been used in agriculture against phytopathogens.Currently,only very few species of the genus were applied to or involved in plant disease control.Discovery of additional useful resources is desperately needed.In this study,biocontrol effect of Trichoderma vermifimicola strain TC467 was evaluated by dual confrontation culture,cellophane and two-compartment culture,pot experiments,and resistance to chemical fungicides.The results demonstrated that TC467 produced substances essential to phytopathogen control(including siderophore,xylanase and chitinase)and plant growth promoters(producing indole-3-acetic acid and gibberellin).The strain displayed a high inhibition rate against Botrytis cinerea reaching 85.26%;and its non-volatile and volatile secondary metabolites showed the inhibition rates to Sclerotinia sclerotiorum and B.cinerea as high as 84.67%and 47.62%,respectively.In pot experiments,comparing with untreated plants TC467 significantly enhanced the height and fresh weight of lettuce(Lactuca sativa var.ramosa)by 46.69%and 15.33%,respectively.Its fermentation broth effectively minimized the lettuce disease caused by B.cinerea with inhibition rate of 87.76%.In addition,the strain showed higher tolerance to hymexazol water-dispersible granule than that to other tested fungicides;at the concentration of 0.42 mg/L the growth rate of TC467 can even approach 98.19%.T.vermifimicola strain TC467 has the potential for practical application in biocontrol especially plant diseases caused by B.cinerea,which extends our knowledge of nature beneficial resources.展开更多
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
Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of t...Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.展开更多
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
Fungal plant diseases are infections caused by pathogenic fungi that affect crops,ornamental plants,and trees.Symptoms of these diseases can include leaf spots,fruit rot,root rot,and generalized growth retardation.Fun...Fungal plant diseases are infections caused by pathogenic fungi that affect crops,ornamental plants,and trees.Symptoms of these diseases can include leaf spots,fruit rot,root rot,and generalized growth retardation.Fungal diseases can result in decreased quality and quantity of crops,which can have a negative economic impact on farmers and producers.Moreover,these diseases can cause environmental damage.Indeed,fungal diseases can directly affect crops by reducing plant growth and yield,as well as altering their quality and nutritional value.Although effective,the use of many chemical products is often harmful to health and the environment,and their use is increasingly restricted due to their high toxicity.To address this issue,it is becoming increasingly essential to replace these chemical products with products that respect the environment and human health,and for sustainable agriculture,such as regenerative agricultural practices.Regenerative agricultural practices such as crop rotation,intercropping,composting,and notill farming techniques can offer sustainable solutions for the prevention and control of plant fungal diseases.These regenratives approaches not only help to control fungal plant disease by strengthening plant disease resistance,but also significantly contribute to the improvement of sustainable agriculture,by restoring soil health,increasing biodiversity and reducing the use of harmful chemicals to the environment and human health in order to keep a long-termecosystem resilience,promote environmental sustainability,and support global food security.Using regenerative agricultural practices can provide a holistic and effective approach to controlling fungal plant diseases while improving the health and productivity of farming systems.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
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
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
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
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
To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and...To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and quantity of food.Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food.Still these mechanisms require manual efforts and human expertise to diagnose diseases.In the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic harvesting.Machine Learning techniques are becoming popular to process images and identify different objects.We can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop losses.In this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and fruits.The proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach crops.For the detection of diseases in Peaches VGG-19 architecture is utilized.For the localization of disease regions Mask R-CNN is utilized.The proposed technique is evaluated using different techniques and has demonstrated 94%accuracy.We hope that the system can help farmers to increase peach production to meet food demands.展开更多
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 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Across all Russia global climate change is observed. Consequences of climatic changes, undoubtedly, will be reflected in distribution of harmful organisms, their injuriousness and will demand development of new approaches in plant protection. Over the last 10 years, the spread of cereal crop diseases in the Northwest Russia has been monitored. The purpose of researches is to find new diseases in the Northwest region of Russia. Disease progression was mainly monitored 3 or 4 times during the growing season, from germination to crop maturity. As a result in this region the new diseases were found. In 2005-2007 the causal agent of yellow leaf spot Pyrenophora tritici-repentis was found on wheat. Fusarium graminearum historically has two areas in Russia： the North Caucasus and the Far East. However, since 2003 F. graminearum appeared on the territory of the North-West of Russia. Septoria tritici became the main pathogen of wheat in the North-Western Region.. In 2013 Ramularia collo-cygni was found in Arkhangelsk region. These observations suggest that global warming of climate leads to an expansion south species pathogen to the north regions of Russia.
文摘The deeper the degree of urbanization, the higher the level of landscaping in this area. The degree of development of urban landscaping reflects the economic level and development of the city. With the country paying more and more attention to the construction of ecological civilization, more and more cities have speeded up the development of landscaping. Landscape construction is not only beneficial to the construction of ecological civilization and the improvement of urban environment, but also adds a bit of beautiful scenery to cities. Under the background of ecological civilization construction, this paper makes some explorations on the related problems of prevention and control of plant diseases and insect pests in gardens, so as to better solve the problem of plant diseases and insect pests and promote the construction of ecological civilization in cities.
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.
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 Key R&D Project of Hubei Province(2023BBB176)Wuhan Knowledge Innovation Special Project‘Dawning Program’(2023020201020433)the authors also gratefully acknowledge the partial support from Hubei Agricultural Science Innovation Center(2024-620-000-001-023).
文摘The incidence of plant bacterial diseases has been rising annually alongside the development of facility agriculture,which can seriously affect the yield and quality of crops,and cause significant losses to agricultural production globally.Thus,the development of highly effective prevention and control agents targeting plant pathogenic bacteria are urgently needed.The Type III secretion system(T3SS)is a complex molecular apparatus exist in many Gram-negative pathogenic bacteria,which plays a crucial role in the pathogenic process of bacteria.In recent years,T3SS has become an important target for the development of novel antibacterial agents.Here,we highlight the recent findings on a plant defense compound targeting T3SS for plant bacterial diseases by Lei and his colleagues.
文摘Trichoderma strains possessing biological control functions have been used in agriculture against phytopathogens.Currently,only very few species of the genus were applied to or involved in plant disease control.Discovery of additional useful resources is desperately needed.In this study,biocontrol effect of Trichoderma vermifimicola strain TC467 was evaluated by dual confrontation culture,cellophane and two-compartment culture,pot experiments,and resistance to chemical fungicides.The results demonstrated that TC467 produced substances essential to phytopathogen control(including siderophore,xylanase and chitinase)and plant growth promoters(producing indole-3-acetic acid and gibberellin).The strain displayed a high inhibition rate against Botrytis cinerea reaching 85.26%;and its non-volatile and volatile secondary metabolites showed the inhibition rates to Sclerotinia sclerotiorum and B.cinerea as high as 84.67%and 47.62%,respectively.In pot experiments,comparing with untreated plants TC467 significantly enhanced the height and fresh weight of lettuce(Lactuca sativa var.ramosa)by 46.69%and 15.33%,respectively.Its fermentation broth effectively minimized the lettuce disease caused by B.cinerea with inhibition rate of 87.76%.In addition,the strain showed higher tolerance to hymexazol water-dispersible granule than that to other tested fungicides;at the concentration of 0.42 mg/L the growth rate of TC467 can even approach 98.19%.T.vermifimicola strain TC467 has the potential for practical application in biocontrol especially plant diseases caused by B.cinerea,which extends our knowledge of nature beneficial resources.
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.
文摘Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.
基金supported 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.
基金provided by SIRAM project within the framework of PRIMA,a program supported by H2020,the European Program for Research and Innovation and the Tunisian Ministry of Higher Education and Scientific Research(MERS).
文摘Fungal plant diseases are infections caused by pathogenic fungi that affect crops,ornamental plants,and trees.Symptoms of these diseases can include leaf spots,fruit rot,root rot,and generalized growth retardation.Fungal diseases can result in decreased quality and quantity of crops,which can have a negative economic impact on farmers and producers.Moreover,these diseases can cause environmental damage.Indeed,fungal diseases can directly affect crops by reducing plant growth and yield,as well as altering their quality and nutritional value.Although effective,the use of many chemical products is often harmful to health and the environment,and their use is increasingly restricted due to their high toxicity.To address this issue,it is becoming increasingly essential to replace these chemical products with products that respect the environment and human health,and for sustainable agriculture,such as regenerative agricultural practices.Regenerative agricultural practices such as crop rotation,intercropping,composting,and notill farming techniques can offer sustainable solutions for the prevention and control of plant fungal diseases.These regenratives approaches not only help to control fungal plant disease by strengthening plant disease resistance,but also significantly contribute to the improvement of sustainable agriculture,by restoring soil health,increasing biodiversity and reducing the use of harmful chemicals to the environment and human health in order to keep a long-termecosystem resilience,promote environmental sustainability,and support global food security.Using regenerative agricultural practices can provide a holistic and effective approach to controlling fungal plant diseases while improving the health and productivity of farming systems.
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.
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The authors received funding for this study from Taif University Researchers Supporting Project No.(TURSP-2020/254),Taif University,Taif,Saudi Arabia.
文摘To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and quantity of food.Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food.Still these mechanisms require manual efforts and human expertise to diagnose diseases.In the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic harvesting.Machine Learning techniques are becoming popular to process images and identify different objects.We can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop losses.In this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and fruits.The proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach crops.For the detection of diseases in Peaches VGG-19 architecture is utilized.For the localization of disease regions Mask R-CNN is utilized.The proposed technique is evaluated using different techniques and has demonstrated 94%accuracy.We hope that the system can help farmers to increase peach production to meet food demands.
