Detection and Identification of Phytoplasma of Cleome rutidosperma in Areca Palm Yellow Leaf Disease Field

[Objectives]The paper was to detect and identify the phytoplasma of Cleome rutidosperma in areca palm yellow leaf disease(YLD)field in Wenchang City,Hainan Province,China.[Methods]The nested PCR technique was employed to amplify the phytoplasma 16S rDNA of C.rutidosperma samples,followed by sequence analysis.Concurrently,this study examined C.rutidosperma in YLD field,collecting symptomatic leaves for phytoplasma detection.[Results]The 16S rDNA sequence of the C.rutidosperma witches'-broom phytoplasma was found to be identical to that of the HNWC5 strain associated with areca palm yellows phytoplasma,leading to the identification of this phytoplasma as belonging to the 16SrII-A subgroup.Field investigations revealed a higher incidence of C.rutidosperma in areca palm fields,with symptoms of leaf yellows observed in six of these fields.Quantitative PCR(qPCR)analysis confirmed the presence of phytoplasma infection in these instances.[Conclusions]Through the analysis of geographical distribution,sequence alignment,and field occurrence data,a significant correlation has been identified between witches'broom disease and YLD.It is proposed that the former may act as an intermediate host for the areca palm yellows phytoplasma.

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

Identification of banana leaf disease based on KVA and GR-ARNet

Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Establishment and Application of Prevention and Control Techniques of New Sugarcane White Leaf Disease

Aiming at the basic and key technical problems in prevention and control of sugarcane white leaf disease(SCWL),this study systematically overcame key technical bottleneck of prevention and control of new SCWL after 10 years of collaborative research,and achieved several innovative achievements.SCWL phytoplasmas newly recorded in China and the new subgroup of SCWL phytoplasmas(16SrXI-D)were discovered for the first time in Yunnan,and the whole genome analysis of the epidemic subgroup was completed.The main transmission source of SCWL pathogens has been identified as infected seed canes,and Tettigoniella viridis and Clovia conifer were newly discovered as vectors for virus transmission.The disease resistance of 25 main varieties was identified,and 10 control varieties were selected.The prevention and control strategy of"emphasizing early warning,strictly carrying out quarantine,blocking the vectors and controlling residual plants"was put forward,and a comprehensive prevention technique was established through integration of various techniques,and standardized technical regulations were formulated for demonstration application.The promotion and application of these achievements have realized scientific prevention and control of SCWL,effectively curbed the spread of SCWL,and ensured the safety of sugarcane producing areas in China,achieving great economic,social and ecological benefits and providing technical support for high-quality development,loss reduction and efficiency improvement of China's sugar industry.

Detection of Cocoa Leaf Diseases Using the CNN-Based Feature Extractor and XGBOOST Classifier

Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.

A novel transcription factor FnMYB4 regulates pigments metabolism of yellow leaf mutants in Fragaria nilgerrensis

The strawberry species Fragaria nilgerrensis Schlechtendal ex J.Gay,renowned for its distinctive white,fragrant peach-like fruits and strong disease resistance,is an exceptional research material.In a previous study,an ethyl methane sulfonate(EMS)mutant library was established for this species,resulting in various yellow leaf mutants.Leaf yellowing materials are not only the ideal materials for basic studies on photosynthesis mechanism,chloroplast development,and molecular regulation of various pigments,but also have important utilization value in ornamental plants breeding.The present study focused on four distinct yellow leaf mutants:mottled yellow leaf(MO),yellow green leaf(YG),light green leaf(LG),and buddha light leaf(BU).The results revealed that the flavonoid content and carotenoid-to-chlorophyll ratio exhibited a significant increase among these mutants,while experiencing a significant decrease in chlorophyll and carotenoid contents compared to the wild type(WT).To clarify the regulatory mechanisms and network relationships underlying these mutants,the RNA-seq and weighted gene coexpression network(WGCNA)analyses were employed.The results showed flavonoid metabolism pathway was enriched both in MO and YG mutants,while the chlorophyll biosynthesis pathway and carotenoid degradation pathway were only enriched in MO and YG mutants,respectively.Subsequently,key structural genes and transcription factors were identified on metabolic pathways of three pigments through correlation analyses and quantitative experiments.Furthermore,a R2R3-MYB transcription factor,FnMYB4,was confirmed to be positively correlated with flavonoid synthesis through transient overexpression,virus-induced gene silencing(VIGS),and RNA interference(RNAi),accompanying by reoccurrence and attenuation of mutant phenotype.Finally,dual-luciferase(LUC)and yeast one-hybrid assays confirmed the binding of FnMYB4 to the FnFLS and FnF3H promoters,indicating that FnMYB4 positively regulates flavonoid synthesis.In addition,correlation analyses suggested that FnMYB4 also might be involved in chlorophyll and carotenoid metabolisms.These findings demonstrated the pivotal regulatory role of FnMYB4 in strawberry leaf coloration.

MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves

The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing costs.MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases.However,current DL methods often require substantial computational resources,hindering their application on resource-constrained devices.We propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this.The Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification.The proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet approach.More specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato diseases.The model could be used on mobile platforms because it is lightweight and designed with fewer layers.Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

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.

Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.

A Review of Study of Yellow Water Disease in Tibetan Medicine Theory

Based on the Tibetan medical theory,the relevant information and diagnosis and treatment ideas of yellow water disease are discussed,and Mongolian medicine also takes its own basic medical theory as the starting point to discuss and explain,while traditional Chinese medicine has made less theoretical description of this disease,but there are also some understandings and treatment guidelines.This paper mainly discusses the cognitive aspects of this disease,starting from the essence,analyzes the relationship between this disease and traditional Chinese medicine diseases as well as modern medicine,and makes a theoretical description for a better understanding of the yellow water disease.

Establishment and Application of Identification Methods for Resistance to Sugarcane White Leaf Disease(SCWL)

[Objectives]This study was conducted to establish simple, efficient, stable, standardized and practical identification methods for sugarcane resistance to white leaf disease(SCWL), and promote the breeding for sugarcane resistance to SCWL. [Methods]The identification technology of sugarcane resistance to SCWL was systematically studied and explored from the aspects of sugarcane material treatment and planting, inoculation liquid preparation, inoculation method, disease investigation, grading standard formulation, etc., and two sets of simple, efficient, stable, standardized and practical accurate identification methods for sugarcane resistance to SCWL were created for the first time, namely, the seed cane coating inoculation method and the stem-cutting inoculation method at the growth stage. The seed cane coating inoculation method includes the steps of directly screening SCWL phytoplasma, extracting juice from cane and adding 10 times of sterile water to prepare an inoculation liquid, spraying seed cane on plastic film to keep moisture, planting the inoculated materials in barrels in an insect-proof greenhouse for cultivation, investigating the incidence rate 30 d after inoculation, and evaluating the disease resistance according to the 1-5 level standard. The method of stem-cutting inoculation includes the steps of directly screening sugarcane stems carrying SCWL phytoplasma and adding 10 times of sterile water to prepare an inoculation liquid, cultivating the identification materials in an insect-proof greenhouse, dropping 100 μl of the inoculation liquid into each root incision with a pipette gun at the age of 6 months, investigating the incidence rate 20 d after planting, and evaluating the disease resistance according to the 1-5 level standard. [Results] The two methods are similar to the natural transmission method. After inoculation, SCML occurred significantly, with high sensitivity and good reproducibility. The results of resistance identification were consistent with those of natural disease in the field. Through the two inoculation methods and field natural disease investigation, the resistance of 10 main cultivars to SCML was identified, which was true and reliable. [Conclusions] This study can provide standard varieties for identification of SCML resistance in the future.

A New Disease of Cherry Plum Tree with Yellow Leaf Symptoms Associated with a Novel Phytoplasma in the Aster Yellows Group

A novel phytoplasma was detected in a cherry plum（Prunus cerasifera Ehrh） tree that mainly showed yellow leaf symptom. The tree was growing in an orchard located in Yangling District, Shaanxi Province, China. The leaves started as chlorotic and yellowing along leaf minor veins and leaf tips. Chlorosis rapidly developed to inter-veinal areas with the whole leaf becoming pale yellow in about 1-4 wk. Large numbers of phytoplasma-like bodies（PLBs） were seen under transmission electron microscopy. The majority of the PLBs was spherical or elliptical vesicles, with diameters in range of 0.1-0.6 μm, and distributed in the phloem cells of the infected tissues. A 1 246-bp 16 S ribosomal RNA（rRNA） gene fragment was amplified from DNA samples extracted from the yellow leaf tissues using two phytoplasma universal primer pairs R16mF2/R16mR1 and R16F2n/R16R2. Phylogenetic analysis using the 16 S rRNA gene sequence suggested that the phytoplasma associated with the yellow leaf symptoms belongs to a novel subclade in the aster yellows（AY） group（16SrI group）. Virtual and actual restriction fragment length polymorphism（RFLP） analysis of the 16 S rRNA gene fragment revealed that the phytoplasma was distinguishable from all existing 19 subgroups in the AY group（16SrI） by four restriction sites, Hinf I, Mse I, Sau3 A I and Taq I. The similarity coefficients of comparing the RFLP pattern of the 16 S rRNA gene fragment of this phytoplasma to each of the 19 reported subgroups ranged from 0.73 to 0.87, which indicates the phytoplasma associated with the cherry plum yellow leaf（CPYL） symptoms is probably a distinct and novel subgroup lineage in the AY group（16SrI）. In addition, the novel phytoplasma was experimentally transmitted to periwinkle（Catharanthus roseus） plants from the tree with CPYL symptoms and then back to a healthy 1-yr-old cherry plum tree via dodder（Cuscuta odorata） connections.

Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and other parameters cause these diseases.In this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy.Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval.Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images.It is an efficient andflexible way of retrieving Tea Leaf images.It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP(mean average precision)scores,which is used as a parameter to judge the efficiency of the model.The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor.This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set.The proposed model will examine the input tea leaf image and identify the type of tea leaf disease.The relevant image will be retrieved based on the resulting type of disease.This model is only trained on scarce data as a real-life scenario,making it practical for many applications.

Combined application of Trichoderma harzianum SH2303 and difenoconazole-propiconazolein controlling Southern corn leaf blight disease caused by Cochliobolus heterostrophus in maize

Southern corn leaf blight(SCLB)disease caused by Cochliobolus heterostrophus is one of the major threats to corn production worldwide.The synergistic application of low toxic chemical fungicide and biocontrol agents could improve biocontrol stability and efficiency against plant diseases,which ultimately reduce use of chemical fungicide.Trichoderma spp.,well-known biocontrol fungi have been used to control some foliar diseases.However,few works have been reported on synergistic application of chemical fungicide and Trichoderma against foliar diseases.This study was aimed to investigate the control effect on the synergistic application of Trichoderma harzianum SH2303 and difenoconazole-propiconazole(DP)against SCLB.Results showed that the synergistic application of DP and SH2303 reduced the leaf spot area compared to the control.The efficacy of synergistic application of DP+SH2303 against SCLB could last for 15–20 d in pot trial under the greenhouse condition.Under the natural field condition,maize treated with DP+DP and DP+SH2303 showed 60%control,which was higher than that of SH2303+DP(45%)and SH2303+SH2303(35%).All these treatments induced the synthesis of defense-related enzymes(phenylalanine ammonia lyase(PAL),catalase(CAT),and superoxide dismutase(SOD))and the defence-related gene expression of SA pathway(PR1).Taken together the in-vitro leaf test and field trial,the control of SCLB by synergistic application of DP+SH2303 was similar to that of DP+DP.Among synergistic application,the sequential application of DP+SH2303 showed better control than the sequential application of SH2303+DP.It was concluded that the synergistic application of chemical fungicide(DP)and biocontrol agent(T.harzianum SH2303)could be used to reduce the chemical fungicide and to reduce the SCLB diseases in maize,which provided alternative approach to realize an eco-friendly controlling of the foliar disease.

A Clinical Study of Safflower Yellow Injection in Treating Coronary Heart Disease Angina Pectoris with Xin-Blood Stagnation Syndrome

Objective： To evaluate the clinical effect and safety of Safflower Yellow injection （SYI） in treating coronary heart disease angina pectoris （OHD-AP） with Xin-blood stagnation syndrome （XBSS）. Methods： Adopted was the multi-centered, randomized, positive parallel controlled method, 448 patients with CHD-AP-XBSS were enrolled and divided into two groups, 336 in the tested group treated with SYI and 112 in the control group treated with Salvia injection by intravenous dripping once a day for 14 days, so as to observe the conditions of angina, electrocardiogram, and therapeutic effect on traditinal Chinese medicine （TCM） symptoms as well as the safety of the treatment. Results： The significantly effective rate and total effective rate in the tested group were 60.06% （194/323） and 91.02 % （294/323） respectively; those in improvement of TOM symptoms were 40. 18% （129/321） and 75.23% （243/323） respectively, which were better than those in the control group （P〈0.01）. Conclusion： SYI Injection is effective and safe in treating OHD-AP-XBSS.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.