Analysis and Study on Characteristics and Detection Methods of Cotton Diseases and Insect Pests

[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,and the aerial data map was converted into geotif image with longitude and latitude and then inputted into the detection system for preprocessing,mainly for image feature extraction and classification.Through deep learning of MATLAB software and BP neural network algorithm,the feature similarity of the images in the established characteristic database of cotton diseases and insect pests was compared.[Results]Through comparative analysis of characteristics of a large number of diseases and insect pests,it was found that deep learning method had high discrimination accuracy and good reliability.[Conclusions]The dynamic detection system using deep learning can well find cotton diseases and insect pests,and achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

Identification and Control of Main Diseases of Cotton

The main symptoms of cotton viral diseases,bacterial diseases,fungal diseases and physiological diseases are introduced,and the corresponding prevention and control techniques are put forward,in order to provide a certain basis for the improvement of cotton yield and quality.

Evaluation on Diseases Resistance of Cotton Material and Its Utilization

Fusarium wilt and Verticillium wilt are important worldwide fungal diseases on cotton that cause damage to yield and quality.The pathogens survive in soil as microsclerotia for many years,and

Studies on Cotton Breeding Resistant to Fusarium and Verticillium wilt Diseases

Both Fusarium and Verticillium wilts are important soil-borne diseases,which can not be effectively controlled by chemical fungicides.The two diseases,especially Verticillium wilt,have

Transgenic Cotton and Disease Resistance Genes

Success in conventional breeding for resistance to mycotoxin-producing or other phytopathogenic fungi is dependent on the availability of resistance gene(s) in the germplasm.Even when it is available,breeding for disease-resistant crops is very time consuming,especially in perennial crops such

A Serratia marcesens Strains Involved in Cotton (<i>Gossypium hirsutum</i>) Boll Infection by a Prokaryote

A boll infection caused by non-traditional cotton pathogens was first reported to occur in the southeastern U.S. Cotton Belt (year 2000) and has since spread to Texas causing significant yield losses. This study was aimed towards investigating the verde plant bug (Creontiades signatus) link between interior boll disease in Texas, USA. Using glasshouse grown bolls, bacteria recovered from locules with disease symptoms from field-grown cotton bolls caged with the piercing-sucking C. signatus were analyzed for the capacity to inflict the disease. For pathogenicity testing, spontaneously generated rifampicin resistant (Rifr) variants were utilized to track the antibiotic resistant bacterium and deter growth of endophytic and contaminating bacteria. To simulate C. signatus feeding, a needle (31 gauge) was employed to inoculate bolls at 13 - 15 days after flower bloom. Bacterial suspensions ranged from 101 - 106 colony forming units/ml. Field infection symptoms were duplicated after two weeks of bacterial exposure. Infectious strains were best categorized as Serratia marcescens based on traditional carbon utilization and enzyme production testing, and a 99% nucleotide sequence identity of 16S ribosomal DNA. Putative S. marcescens representatives isolated from rotted bolls exposed to C. signatus were shown to reproduce field infection symptoms upon inoculation into greenhouse grown fruit. Serratia spp. can inflict disease in alfalfa, cucurbits, and sunflower. The presented data are the first to definitively show that a Serratia sp. has the capacity to infect cotton.

Cotton leaf disease detection method based on improved SSD

In response to the problems of numerous model parameters and low detection accuracy in SSD-based cotton leaf disease detection methods,a cotton leaf disease detection method based on improved SSD was proposed by combining the characteristics of cotton leaf diseases.First,the lightweight network MobileNetV2 was introduced to improve the backbone feature extraction network,which provides more abundant semantic information and details while significantly reducing the amount of model parameters and computing complexity,and accelerates the detection speed to achieve real-time detection.Then,the SE attention mechanism,ECA attention mechanism,and CBAM attention mechanism were fused to filter out disease target features and effectively suppress the feature information of jamming targets,generating feature maps with strong semantics and precise location information.The test results on the self-built cotton leaf disease dataset show that the parameter quantity of the SSD_MobileNetV2 model with backbone network of MobileNetV2 was 50.9%of the SSD_VGG model taking VGG as the backbone.Compared with SSD_VGG model,the P,R,F1 values,and mAP of the MobileNetV2 model increased by 4.37%,3.3%,3.8%,and 8.79%respectively,while FPS increased by 22.5 frames/s.The SE,ECA,and CBAM attention mechanisms were introduced into the SSD_VGG model and SSD_MobileNetV2 model.Using gradient weighted class activation mapping algorithm to explain the model detection process and visually compare the detection results of each model.The results indicate that the P,R,F1 values,mAP and FPS of the SSD_MobileNetV2+ECA model were higher than other models that introduced the attention mechanisms.Moreover,this model has less parameter with faster running speed,and is more suitable for detecting cotton diseases in complex environments,showing the best detection effect.Therefore,the improved SSD_MobileNetV2+ECA model significantly enhanced the semantic information of the shallow feature map of the model,and has a good detection effect on cotton leaf diseases in complex environments.The research can provide a lightweight,real-time,and accurate solution for detecting of cotton diseases in complex environments.