A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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.

Biological control of rice disease and insect by chitinase-producing bacterium X2-23

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

Rice diseases in China

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.

YOLO-CRD:A Lightweight Model for the Detection of Rice Diseases in Natural Environments

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.

Identification of Rice Leaf Disease Using Improved ShuffleNet V2

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%).

Predicting rice diseases using advanced technologies at different scales: present status and future perspectives

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.

Rice disease identification method based on improved CNN-BiGRU

In the field of precision agriculture,diagnosing rice diseases from images remains challenging due to high error rates,multiple influencing factors,and unstable conditions.While machine learning and convolutional neural networks have shown promising results in identifying rice diseases,they were limited in their ability to explain the relationships among disease features.In this study,we proposed an improved rice disease classification method that combines a convolutional neural network(CNN)with a bidirectional gated recurrent unit(BiGRU).Specifically,we introduced a residual mechanism into the Inception module,expanded the module's depth,and integrated an improved Convolutional Block Attention Module(CBAM).We trained and tested the improved CNN and BiGRU,concatenated the outputs of the CNN and BiGRU modules,and passed them to the classification layer for recognition.Our experiments demonstrate that this approach achieves an accuracy of 98.21%in identifying four types of rice diseases,providing a reliable method for rice disease recognition research.

Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

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.

Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

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.

Effect of Rice Sowing Date on Occurrence of Small Brown Planthopper and Epidemics of Planthopper-Transmitted Rice Stripe Viral Disease

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.

Transferring Translucent Endosperm Mutant Gene Wx-mq and Rice Stripe Disease Resistance Gene Stv-bi by Marker-Assisted Selection in Rice (Oryza sativa)

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.

An Optimal Classification Model for Rice Plant Disease Detection

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.

RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

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.

An Efficient Disease Detection Technique of Rice Leaf Using AlexNet

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.

Genome-Wide Analysis of von Willebrand Factor A Gene Family in Rice for Its Role in Imparting Biotic Stress Resistance with Emphasis on Rice Blast Disease

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.

Disaster Prevention and Mitigation Technologies of Rice Virus Diseases Spread by Small Brown Planthopper (SBPH)

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.

Prevalence Characteristics of Rice Black-streaked Dwarf Virus Disease and Continuous Control Strategies

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.

Studies on Pathogenie Fusarium species of rice Bakanae disease and strains

We collected infection plants of rice bakanae desiease from 22 counties (cities) in Hubei Province in 1990-1992. Thirty five isolates of single spore were isolated from the plants. Fusarium species were identified from these isolates based on the method of Booth(1971) and other authors. Six kinds of medium wereused in the experiment. Color and growth rate ofcolonies as well as other characters were determinedfrom cultures grown on PSA. The results showedthat thirty one isolates were Fusarium moniliformevar. zhejiangensis, two isolates were Fusariumgramineum, one was Fusariurn oxysporum, and onewas Fusarium solani.