In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the s...In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.展开更多
Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental pollution.Accurate detection of rice diseases in natural environments is crucial...Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental pollution.Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance.Deep learning-based disease identification technologies have shown promise in automatically discerning disease types.However,effectively extracting early disease features in natural environments remains a challenging problem.To address this issue,this study proposes the YOLO-CRD method.This research selected images of common rice diseases,primarily bakanae disease,bacterial brown spot,leaf rice fever,and dry tip nematode disease,from Tianjin Xiaozhan.The proposed YOLO-CRD model enhanced the YOLOv5s network architecture with a Convolutional Channel Attention Module,Spatial Pyramid Pooling Cross-Stage Partial Channel module,and Ghost module.The former module improves attention across image channels and spatial dimensions,the middle module enhances model generalization,and the latter module reduces model size.To validate the feasibility and robustness of this method,the detection model achieved the following metrics on the test set:mean average precision of 90.2%,accuracy of 90.4%,F1-score of 88.0,and GFLOPS of 18.4.for the specific diseases,the mean average precision scores were 85.8%for bakanae disease,93.5%for bacterial brown spot,94%for leaf rice fever,and 87.4%for dry tip nematode disease.Case studies and comparative analyses verified the effectiveness and superiority of the proposed method.These researchfind-ings can be applied to rice disease detection,laying the groundwork for the development of automated rice disease detection equipment.展开更多
According to incomplete records, there are about 50 fungus diseases of rice in China.Rice blast and sheath blight are the most destructive and cause severe damage. Other fungus diseases, such as rot, narrow brown leaf...According to incomplete records, there are about 50 fungus diseases of rice in China.Rice blast and sheath blight are the most destructive and cause severe damage. Other fungus diseases, such as rot, narrow brown leaf spot, leaf scald and brown spot are not serious. Four bacterial diseases of rice occur in China. Bacterial blight is the most common and destructive one, followed by bacterial leaf streak, rice brown spot and brown stripe.The latter two appear sporadically and are less important. The virus and virus-like diseases of rice that have been recorded in China are rice yellow dwarf, rice dwarf, rice yellow stunt, rice black-streaked dwarf, rice stripe, dwarf-like diseases, rice orange leaf disease, rice transitory yellowing, and grassy stunt etc. They are distributed mainly in the southern part of China beyond the Yangtze River. Rice yellow stunt virus has recently become important and widespread in China. It causes 20-30% of yield losses in areas where it prevails.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
The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image proc...The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image processing techniques using machine learning (ML) and deep learning (DL) models related tomulti-scale rice diseases. Furthermore, we summarize applications of different detection techniques,including genomic, physiological, and biochemical approaches. In addition, we also present the state-ofthe-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. Thisreview serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice cropsthrough ML and DL models.展开更多
In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the trainin...In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.展开更多
Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield.To improve the classification accuracy of rice diseases,this paper proposed a classification and identification met...Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield.To improve the classification accuracy of rice diseases,this paper proposed a classification and identification method based on an improved ShuffleNet V2(GE-ShuffleNet)model.Firstly,the Ghost module is used to replace the 1×1 convolution in the two basic unit modules of ShuffleNet V2,and the unimportant 1×1 convolution is deleted from the two basic unit modules of ShuffleNet V2.The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model.Secondly,an effective channel attention(ECA)module is added to the network to avoid dimension reduction,and the correlation between channels is effectively extracted through 1D convolution.Besides,L2 regularization is introduced to fine-tune the training parameters during training to prevent overfitting.Finally,the considerable experimental and numerical results proved the advantages of our proposed model in terms of model size,floating-point operation per second(FLOPs),and parameters(Params).Especially in the case of smaller model size(5.879 M),the identification accuracy of GE-ShuffleNet(96.6%)is higher than that of ShuffleNet V2(94.4%),MobileNet V2(93.7%),AlexNet(79.1%),Swim Transformer(88.1%),EfficientNet V2(89.7%),VGG16(81.9%),GhostNet(89.3%)and ResNet50(92.5%).展开更多
Rice stripe virus disease (RSVD) and rice black-streaked dwarf virus disease (RBSDVD) are two epidemic diseases in Yancheng City, Jiangsu Province in the last decade. The authors investigated the disaster regulari...Rice stripe virus disease (RSVD) and rice black-streaked dwarf virus disease (RBSDVD) are two epidemic diseases in Yancheng City, Jiangsu Province in the last decade. The authors investigated the disaster regularity, prevention and control technology of RSVD and RBSDVD systematically. The occurrence and virus transmission of SBPH and disaster regularity of virus diseases were studied; the resistance of some rice varieties was cleared; the effects of physical and agricultural measures such as insect net blocking, appropriate late sowing and plowing on controlling occurrence and virus transmission of SBPH were figured out; a hatch of chemical agents were screened, providing efficient and harmless pesticides for effective control against SBPH and prevention against virus diseases. A set of disaster control and mitigation technologies was proposed in this paper, which was practical, sustainable, and easy to operate for the local planting patterns.展开更多
In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of ...In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.展开更多
During 1984-1988,2,231 varieties(lines)from International Rice Testing Program(IRTP)were evaluated and screened for resistance to riceblast(Bl),bacterial blight(BB),sheath blight
To understand the relationship between rice sowing date and occurrence of the rice small brown planthopper (SBPH) Laodelphax striatellus Fallen and the epidemics of the planthopper-transmitted rice stripe viral (RS...To understand the relationship between rice sowing date and occurrence of the rice small brown planthopper (SBPH) Laodelphax striatellus Fallen and the epidemics of the planthopper-transmitted rice stripe viral (RSV) disease, four sowing dates of rice were evaluated in 2006 and 2007. The results showed that the peak density of SBPH and RSV incidence in the nursery and in the transplanted field decreased with the delay of sowing date in single crop of japonica rice in north Zhejiang Province of China. The relationship between seedling RSV incidence at the end of the nursery trial with sowing date was well described by Weibull equation. The area under the curve of population dynamics (AUCPD or planthopper-day accumulation) or the peak density of the planthopper in the nursery could be summarized by a logistic equation. RSV incidence in the transplanted fields could be characterized quantitatively by a multivariate regression equation, including the variables of sowing date, peak density of the vector, and RSV incidence at the end of the nursery trial. That the descriptive model excluded the AUCPD in transplanted field implies that this variable is not necessary in forecasting disease epidemics in the field. The 2-year experiments sufficiently indicated that suitable sowing of rice could be used as one of the effective measures to control the vector population and therefore the planthopper-transmitted RSV on a larger scale. The optimal sowing date for the single-cropped transplanted japonica rice is recommended from late May to early June in north Zhejiang, China.展开更多
A high-yielding japonica rice variety, Wuyunjing 7, bred in Jiangsu Province, China as a female parent was crossed with a Japanese rice variety Kantou 194, which carries a rice stripe disease resistance gene Stv-b' a...A high-yielding japonica rice variety, Wuyunjing 7, bred in Jiangsu Province, China as a female parent was crossed with a Japanese rice variety Kantou 194, which carries a rice stripe disease resistance gene Stv-b' and a translucent endosperm mutant gene Wx-mq. From F2 generations, a sequence characterized amplified region (SCAR) marker tightly linked with Stv-b' and a cleaved amplified polymorphic sequence (CAPS) marker for Wx-mq were used for marker-assisted selection. Finally, a new japonica rice line, Ning 9108, with excellent agronomic traits was obtained by multi-generational selection on stripe disease resistance and endosperm appearance. The utilization of the markers from genes related to rice quality and disease resistance was helpful not only for establishing a marker-assisted selection system of high-quality and disease resistance for rice but also for providing important intermediate materials and rapid selection method for good quality, disease resistance and high yield in rice breeding.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
As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results acc...As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.展开更多
von Willebrand factor A(vWA)genes are well characterized in humans except for few BONZAI genes,but the vWA genes are least explored in plants.Considering the novelty and vital role of vWA genes,this study aimed at cha...von Willebrand factor A(vWA)genes are well characterized in humans except for few BONZAI genes,but the vWA genes are least explored in plants.Considering the novelty and vital role of vWA genes,this study aimed at characterization of vWA superfamily in rice.Rice genome was found to have 40 vWA genes distributed across all the 12 chromosomes,and 20 of the 40 vWA genes were unique while the remaining shared large fragment similarities with each other,indicating gene duplication.In addition to vWA domain,vWA proteins possess other different motifs or domains,such as ubiquitin interacting motif in protein degradation pathway,and RING finger in protein-protein interaction.Expression analysis of vWA genes in available expression data suggested that they probably function in biotic and abiotic stress responses including hormonal response and signaling.The frequency of transposon elements in the entire 3K rice germplasm was negligible except for 9 vWA genes,indicating the importance of these genes in rice.Structural and functional diversities showed that the vWA genes in a blast-resistant rice variety Tetep had huge variations compared to blast-susceptible rice varieties HP2216 and Nipponbare.qRT-PCR analysis of vWA genes in Magnaporthe oryzae infected rice tissues indicated OsvWA9,OsvWA36,OsvWA37 and OsvWA18 as the optimal candidate genes for disease resistance.This is the first attempt to characterize vWA gene family in plant species.展开更多
[ Objective] The paper was to confirm the best application period and frequency of 75% trifloxystrobin ·tebuconazole WG against rice blast and rice sheath blight. [ Method] Influences of different dosages and dif...[ Objective] The paper was to confirm the best application period and frequency of 75% trifloxystrobin ·tebuconazole WG against rice blast and rice sheath blight. [ Method] Influences of different dosages and different application periods of 75% trifloxystrobin · tebuconazole WG on control effects against rice blast and rice sheath blight, as well as their effects on rice yields were studied in the paper. [Result] The control effects of three different fungicides application treatments against rice sheath blight were 80.24%, 83.0% and 67.99%, and the control effects against rice blast were 56.4%, 49.11% and 61.1%, respective- ly. Advanced application of fungicide for two times had good prevention effect against rice sheath blight ; properly delayed application of fungicides for two times was conducive to improving the control effect against rice blast, and one time application of sufficient fungicide during middle booting stage had higher control effect than application for two times. Effective panicle number per unit area, total grain number, 1 000-grain weight and moisture content of various fungicide application treat- ments did not have significant difference with those of the treatments without fungicide application, but grain number per panicle in treatments applied with fungicide was higher than the treatment without application. [ Conclusion] 75% Trifloxystrobin ·tebuconazole WG has better control effect on rice blast and sheath blight, which helps to promote the formation of rice grain and increases yield significantly. The fungicide application against rice sheath blight should be appropriately ad- vanced, and application for one time against panicle blast after middle booting stage is helpful to improve the control effect.展开更多
Enriched by the-medium containingchitin and cell wall of Phizoctoniasolani AG-1,a bacterium X2-23 withhigher chitinase activity was isolatedfrom 166 chitinase-producing bacteria.It could distinctly inhibit the fungi
To assist with rapid screening for rice blast resistance as a precursor in a breeding program, the susceptibility to rice blast of 13 rice genotypes from Australia was evaluated in May to June 2013 using three distinc...To assist with rapid screening for rice blast resistance as a precursor in a breeding program, the susceptibility to rice blast of 13 rice genotypes from Australia was evaluated in May to June 2013 using three distinct inoculation methods(spot, filter paper and standard methods) at seedling, vegetative and reproductive stages. The results revealed that the spot and filter paper inoculation methods were successful in discerning susceptibility to the rice blast disease(P ≤ 0.05). Disease susceptibility declined significantly from the vegetative to reproductive stages. The standard method was conducted at three different stages for pot plants grown inside the mist house. However, low temperatures did not produce disease symptoms except in a few genotypes. Among the 13 rice genotypes screened, AAT9 expressed a highly resistant response, and AAT4, AAT6, AAT10, AAT11, AAT13, AAT17 and AAT18 expressed resistance at various stages. The results will be useful for selecting elite genotypes for disease tolerance where rice blast is prevalent. In addition, the resistant genotypes can serve as a gene pool used in breeding programmes to develop new resistant genotypes.展开更多
Through summarizing the prevalence characteristics of rice black-streaked dwarf virus disease(RBSDVD)in Linyi City of Shandong Province,this paper analyzed its prevalence is related to changes in farming and cultivati...Through summarizing the prevalence characteristics of rice black-streaked dwarf virus disease(RBSDVD)in Linyi City of Shandong Province,this paper analyzed its prevalence is related to changes in farming and cultivation systems,the increase in the population of venomous Laodelphax striatellus Fallén and its own migration and spread,the poor disease resistance of cultivated varieties,and inadequate time of prevention and control.Besides,based on the practice of local control,it came up with some comprehensive control measures including strengthening monitoring,early warning and forecasting,planting resistant(tolerant)rice varieties according to local conditions,appropriately delaying the sowing(planting)period,supplemented by insect nets to cover seedlings,and making scientific use of chemical control.It is expected to provide a reference for the prevention and control of RBSDVD.展开更多
基金funded by Hunan Provincial Natural Science Foundation of China with Grant Numbers(2022JJ50016,2023JJ50096)Innovation Platform Open Fund of Hengyang Normal University Grant 2021HSKFJJ039Hengyang Science and Technology Plan Guiding Project with Number 202222025902.
文摘In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.
基金Tianjin Science and Technology Plan Project(Grant No.21YFSNSN00040)Tianjin Key R&D Plan Science and Technology Support Project(Grant No.20YFZCSN00220)+1 种基金Central Financial Services to Guide Local Science and Technology Development Project(Grant No.21ZYCGSN00590)Tianjin Key Laboratory of Intelligent Crop Breeding Youth Open Project(Grant No.KLIBMC2302).
文摘Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental pollution.Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance.Deep learning-based disease identification technologies have shown promise in automatically discerning disease types.However,effectively extracting early disease features in natural environments remains a challenging problem.To address this issue,this study proposes the YOLO-CRD method.This research selected images of common rice diseases,primarily bakanae disease,bacterial brown spot,leaf rice fever,and dry tip nematode disease,from Tianjin Xiaozhan.The proposed YOLO-CRD model enhanced the YOLOv5s network architecture with a Convolutional Channel Attention Module,Spatial Pyramid Pooling Cross-Stage Partial Channel module,and Ghost module.The former module improves attention across image channels and spatial dimensions,the middle module enhances model generalization,and the latter module reduces model size.To validate the feasibility and robustness of this method,the detection model achieved the following metrics on the test set:mean average precision of 90.2%,accuracy of 90.4%,F1-score of 88.0,and GFLOPS of 18.4.for the specific diseases,the mean average precision scores were 85.8%for bakanae disease,93.5%for bacterial brown spot,94%for leaf rice fever,and 87.4%for dry tip nematode disease.Case studies and comparative analyses verified the effectiveness and superiority of the proposed method.These researchfind-ings can be applied to rice disease detection,laying the groundwork for the development of automated rice disease detection equipment.
文摘According to incomplete records, there are about 50 fungus diseases of rice in China.Rice blast and sheath blight are the most destructive and cause severe damage. Other fungus diseases, such as rot, narrow brown leaf spot, leaf scald and brown spot are not serious. Four bacterial diseases of rice occur in China. Bacterial blight is the most common and destructive one, followed by bacterial leaf streak, rice brown spot and brown stripe.The latter two appear sporadically and are less important. The virus and virus-like diseases of rice that have been recorded in China are rice yellow dwarf, rice dwarf, rice yellow stunt, rice black-streaked dwarf, rice stripe, dwarf-like diseases, rice orange leaf disease, rice transitory yellowing, and grassy stunt etc. They are distributed mainly in the southern part of China beyond the Yangtze River. Rice yellow stunt virus has recently become important and widespread in China. It causes 20-30% of yield losses in areas where it prevails.
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 the Key R&D Plan of Zhejiang Province(2021C02057,2020C02002)the National Key R&D Program of China(2021YFE0113700)+2 种基金the International S&T Cooperation Program of China(2019YFE0103800)Fundamental Research Funds for the Zhejiang Provincial Universities[2021XZZX024]Zhejiang University Global Partnership Fund.We also appreciate Prof.Zhonghua Ma(Institute of Biotechnology,Zhejiang University)for his insightful advice on this work.
文摘The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image processing techniques using machine learning (ML) and deep learning (DL) models related tomulti-scale rice diseases. Furthermore, we summarize applications of different detection techniques,including genomic, physiological, and biochemical approaches. In addition, we also present the state-ofthe-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. Thisreview serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice cropsthrough ML and DL models.
基金National Key Research and Development Program of China(2021ZD0113704).
文摘In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.
基金This work is supported in part by the Ji Lin provincial science and technology department international science and technology cooperation project under Grant 20200801014GHthe Changchun City Science and Technology Bureau key science and technology research projects under Grant 21ZGN28.
文摘Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield.To improve the classification accuracy of rice diseases,this paper proposed a classification and identification method based on an improved ShuffleNet V2(GE-ShuffleNet)model.Firstly,the Ghost module is used to replace the 1×1 convolution in the two basic unit modules of ShuffleNet V2,and the unimportant 1×1 convolution is deleted from the two basic unit modules of ShuffleNet V2.The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model.Secondly,an effective channel attention(ECA)module is added to the network to avoid dimension reduction,and the correlation between channels is effectively extracted through 1D convolution.Besides,L2 regularization is introduced to fine-tune the training parameters during training to prevent overfitting.Finally,the considerable experimental and numerical results proved the advantages of our proposed model in terms of model size,floating-point operation per second(FLOPs),and parameters(Params).Especially in the case of smaller model size(5.879 M),the identification accuracy of GE-ShuffleNet(96.6%)is higher than that of ShuffleNet V2(94.4%),MobileNet V2(93.7%),AlexNet(79.1%),Swim Transformer(88.1%),EfficientNet V2(89.7%),VGG16(81.9%),GhostNet(89.3%)and ResNet50(92.5%).
基金Supported by Agricultural "Three-item" Project of Jiangsu Province "Promotion of Comprehensive Prevention and Control Technology of Major Pests and Diseases such as Rice Stripe Virus Disease" [SX(2005)029] & Agricultural "Three-item" Project of Jiangsu Province "Prevention and Control Technology of Rice Black-streaked Dwarf Virus Disease"[SX(2008)018] & Agricultural "Three-item" Project of Jiangsu Province "Integration and Popularization of Prevention and Control Technology of Rice Black-streaked Dwarf Virus Disease"[SX(2009)49]
文摘Rice stripe virus disease (RSVD) and rice black-streaked dwarf virus disease (RBSDVD) are two epidemic diseases in Yancheng City, Jiangsu Province in the last decade. The authors investigated the disaster regularity, prevention and control technology of RSVD and RBSDVD systematically. The occurrence and virus transmission of SBPH and disaster regularity of virus diseases were studied; the resistance of some rice varieties was cleared; the effects of physical and agricultural measures such as insect net blocking, appropriate late sowing and plowing on controlling occurrence and virus transmission of SBPH were figured out; a hatch of chemical agents were screened, providing efficient and harmless pesticides for effective control against SBPH and prevention against virus diseases. A set of disaster control and mitigation technologies was proposed in this paper, which was practical, sustainable, and easy to operate for the local planting patterns.
基金supported by National key research and development program sub-topics[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]Jilin Province Science and Technology Development Plan focuses on research and development projects[20200402006NC(Mu Y.,Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn]+1 种基金Science and technology support project for key industries in southern Xinjiang[2018DB001(Gong H.,and Li S.J.)http://kjj.xjbt.gov.cn]Key technology R&D project of Changchun Science and Technology Bureau of Jilin Province[21ZGN29(Mu Y.,Bao H.P.,Wang X.B.)http://kjj.changchun.gov.cn].
文摘In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.
文摘During 1984-1988,2,231 varieties(lines)from International Rice Testing Program(IRTP)were evaluated and screened for resistance to riceblast(Bl),bacterial blight(BB),sheath blight
基金financially supported by the National High-Tech R&D Program of China (863 Program,2007AA10Z220)National Key Technologies R&D Program during the 11th Five-Year Plan period of China(2006BAD17B06)+3 种基金the National Basic Research Program of China (973 Program, 2002CB111400)Zhejiang Provincial Key Project (2009CB119203)Zhejiang Yangtze Delta Key Sci & Tech Collaborative Program,China (2004E60055)Jiaxing City Key Sci & Tech Project, China (2005AZ3002)
文摘To understand the relationship between rice sowing date and occurrence of the rice small brown planthopper (SBPH) Laodelphax striatellus Fallen and the epidemics of the planthopper-transmitted rice stripe viral (RSV) disease, four sowing dates of rice were evaluated in 2006 and 2007. The results showed that the peak density of SBPH and RSV incidence in the nursery and in the transplanted field decreased with the delay of sowing date in single crop of japonica rice in north Zhejiang Province of China. The relationship between seedling RSV incidence at the end of the nursery trial with sowing date was well described by Weibull equation. The area under the curve of population dynamics (AUCPD or planthopper-day accumulation) or the peak density of the planthopper in the nursery could be summarized by a logistic equation. RSV incidence in the transplanted fields could be characterized quantitatively by a multivariate regression equation, including the variables of sowing date, peak density of the vector, and RSV incidence at the end of the nursery trial. That the descriptive model excluded the AUCPD in transplanted field implies that this variable is not necessary in forecasting disease epidemics in the field. The 2-year experiments sufficiently indicated that suitable sowing of rice could be used as one of the effective measures to control the vector population and therefore the planthopper-transmitted RSV on a larger scale. The optimal sowing date for the single-cropped transplanted japonica rice is recommended from late May to early June in north Zhejiang, China.
基金supported by the Key Program of the Development of Variety of Genetically Modified Organisms(Grant Nos.2009ZX08001-019B and 2008ZX08001-006)the Special Program for Rice Scientific Research of Ministry of Agriculture(Grant No.nyhyzx 07-001-006)+1 种基金the Key Support Program of Science and Technology of Jiangsu Province(Grant No.BE2008354)the Self-directed Innovation Fund of Agricultural Science and Technology in Jiangsu Province,China(Grant No.CX[09]634)
文摘A high-yielding japonica rice variety, Wuyunjing 7, bred in Jiangsu Province, China as a female parent was crossed with a Japanese rice variety Kantou 194, which carries a rice stripe disease resistance gene Stv-b' and a translucent endosperm mutant gene Wx-mq. From F2 generations, a sequence characterized amplified region (SCAR) marker tightly linked with Stv-b' and a cleaved amplified polymorphic sequence (CAPS) marker for Wx-mq were used for marker-assisted selection. Finally, a new japonica rice line, Ning 9108, with excellent agronomic traits was obtained by multi-generational selection on stripe disease resistance and endosperm appearance. The utilization of the markers from genes related to rice quality and disease resistance was helpful not only for establishing a marker-assisted selection system of high-quality and disease resistance for rice but also for providing important intermediate materials and rapid selection method for good quality, disease resistance and high yield in rice breeding.
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.
文摘As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.
基金the Indian Council of Agricultural Research(ICAR)-National Institute for Plant Biotechnology,National Agricultural Higher Education Project:Centre for Advanced Agricultural Science and Technology(Grant No.1010033)ICAR-Centre for Agricultural Bioinformatics,Indian Agricultural Statistics Research Institute,New Delhi(IASRI)(Grant No.1006456).
文摘von Willebrand factor A(vWA)genes are well characterized in humans except for few BONZAI genes,but the vWA genes are least explored in plants.Considering the novelty and vital role of vWA genes,this study aimed at characterization of vWA superfamily in rice.Rice genome was found to have 40 vWA genes distributed across all the 12 chromosomes,and 20 of the 40 vWA genes were unique while the remaining shared large fragment similarities with each other,indicating gene duplication.In addition to vWA domain,vWA proteins possess other different motifs or domains,such as ubiquitin interacting motif in protein degradation pathway,and RING finger in protein-protein interaction.Expression analysis of vWA genes in available expression data suggested that they probably function in biotic and abiotic stress responses including hormonal response and signaling.The frequency of transposon elements in the entire 3K rice germplasm was negligible except for 9 vWA genes,indicating the importance of these genes in rice.Structural and functional diversities showed that the vWA genes in a blast-resistant rice variety Tetep had huge variations compared to blast-susceptible rice varieties HP2216 and Nipponbare.qRT-PCR analysis of vWA genes in Magnaporthe oryzae infected rice tissues indicated OsvWA9,OsvWA36,OsvWA37 and OsvWA18 as the optimal candidate genes for disease resistance.This is the first attempt to characterize vWA gene family in plant species.
基金Supported by National Science and Technology Projects (2012BAD19B03).
文摘[ Objective] The paper was to confirm the best application period and frequency of 75% trifloxystrobin ·tebuconazole WG against rice blast and rice sheath blight. [ Method] Influences of different dosages and different application periods of 75% trifloxystrobin · tebuconazole WG on control effects against rice blast and rice sheath blight, as well as their effects on rice yields were studied in the paper. [Result] The control effects of three different fungicides application treatments against rice sheath blight were 80.24%, 83.0% and 67.99%, and the control effects against rice blast were 56.4%, 49.11% and 61.1%, respective- ly. Advanced application of fungicide for two times had good prevention effect against rice sheath blight ; properly delayed application of fungicides for two times was conducive to improving the control effect against rice blast, and one time application of sufficient fungicide during middle booting stage had higher control effect than application for two times. Effective panicle number per unit area, total grain number, 1 000-grain weight and moisture content of various fungicide application treat- ments did not have significant difference with those of the treatments without fungicide application, but grain number per panicle in treatments applied with fungicide was higher than the treatment without application. [ Conclusion] 75% Trifloxystrobin ·tebuconazole WG has better control effect on rice blast and sheath blight, which helps to promote the formation of rice grain and increases yield significantly. The fungicide application against rice sheath blight should be appropriately ad- vanced, and application for one time against panicle blast after middle booting stage is helpful to improve the control effect.
文摘Enriched by the-medium containingchitin and cell wall of Phizoctoniasolani AG-1,a bacterium X2-23 withhigher chitinase activity was isolatedfrom 166 chitinase-producing bacteria.It could distinctly inhibit the fungi
文摘To assist with rapid screening for rice blast resistance as a precursor in a breeding program, the susceptibility to rice blast of 13 rice genotypes from Australia was evaluated in May to June 2013 using three distinct inoculation methods(spot, filter paper and standard methods) at seedling, vegetative and reproductive stages. The results revealed that the spot and filter paper inoculation methods were successful in discerning susceptibility to the rice blast disease(P ≤ 0.05). Disease susceptibility declined significantly from the vegetative to reproductive stages. The standard method was conducted at three different stages for pot plants grown inside the mist house. However, low temperatures did not produce disease symptoms except in a few genotypes. Among the 13 rice genotypes screened, AAT9 expressed a highly resistant response, and AAT4, AAT6, AAT10, AAT11, AAT13, AAT17 and AAT18 expressed resistance at various stages. The results will be useful for selecting elite genotypes for disease tolerance where rice blast is prevalent. In addition, the resistant genotypes can serve as a gene pool used in breeding programmes to develop new resistant genotypes.
基金the Genetic Breeding Post Construction Project of Rice Innovation Team for Modern Agricultural Industrial Technology System in Shandong Province of China(SDAIT-17-02).
文摘Through summarizing the prevalence characteristics of rice black-streaked dwarf virus disease(RBSDVD)in Linyi City of Shandong Province,this paper analyzed its prevalence is related to changes in farming and cultivation systems,the increase in the population of venomous Laodelphax striatellus Fallén and its own migration and spread,the poor disease resistance of cultivated varieties,and inadequate time of prevention and control.Besides,based on the practice of local control,it came up with some comprehensive control measures including strengthening monitoring,early warning and forecasting,planting resistant(tolerant)rice varieties according to local conditions,appropriately delaying the sowing(planting)period,supplemented by insect nets to cover seedlings,and making scientific use of chemical control.It is expected to provide a reference for the prevention and control of RBSDVD.