Image Recognition of Citrus Diseases Based on Deep Learning

In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in farming.In Pakistan,several kinds of fruits are produced and exported the other countries.Citrus is an important fruit,and its production in Pakistan is higher than the other fruits.However,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this Fruit.The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure.In the agriculture sector,deep learning showing significant success in the last five years.This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification.In the proposed framework,the augmentation technique is applied initially by creating more training data from existing samples.They were then modifying the two pre-trained models named Resnet18 and Inception V3.The modified models are trained using an augmented dataset through transfer learning.Features are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few more.The experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus Fruits.On these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,respectively.The proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.

Survey and Detection of Disease and Pest Occurrence in Citrus Nurseries

[Objectives]The paper was to ascertain the prevalence of diseases and pests in a range of citrus nurseries situated in Guangdong Province and its neighboring provinces.[Methods]Citrus diseases and pests were systematically investigated,and citrus leaf samples were randomly collected from 15 citrus nurseries across 8 regions in Guangdong Province and its neighboring provinces.Quantitative polymerase chain reaction(qPCR)and reverse transcription polymerase chain reaction(RT-PCR)techniques were employed to detect diseases in the collected samples.Additionally,root and substrate samples were obtained,and root-knot nematodes were isolated using the Baermann funnel method.[Results]The positive detection rate of citrus huanglongbing(HLB)was recorded at 3%,indicating an increase in attention towards this disease compared to 2013.Additionally,the positive detection rate for citrus bacterial canker disease(CBCD)was found to be 16.5%.It was observed that the majority of nurseries with positive samples employed open field rearing practices without the use of mesh chambers,and the primary source of scions was self-propagation.The detection rate of citrus tristeza virus(CTV)was found to be the highest,with a positive detection rate of 63%,and the prevalence in disease-bearing nurseries reached as high as 90%.In comparison to 2013,there had been no improvement in the condition of seedlings affected by CTV.The positive detection rate of citrus yellow vein clearing virus(CYVCV)was found to be 38%,with 70%of the surveyed nurseries exhibiting the disease.The citrus varieties identified as carriers of the disease included‘Qicheng’,‘Shatangju’,‘Wogan’,and‘Gonggan’.Nematodes were isolated from the matrix and roots of seedlings grown in both container and open field environments.The susceptibility of container seedlings to nematodes was found to be 36.4%,while the susceptibility of open field seedlings was 38.6%.Statistical analysis indicated no significant difference in susceptibility between the two groups.[Conclusions]The disease detection rates associated with various seedling rearing methods and citrus varieties exhibited notable variability.Open field seedlings without the protection of mesh chambers demonstrated a higher susceptibility to disease.Additionally,the types of infectious diseases varied among the different citrus varieties.

Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

Phyllosticta species associated with citrus diseases in China

Phyllosticta species associated with diseases of four commercial Citrus species grown in China are reported.Totally,496 Phyllosticta strains were isolated from mandarins(Citrus reticulata),pomeloes(C.maxima),oranges(C.sinensis)and lemons(C.limon)in the main citrus producing regions across China,and 74 strains were selected for phylogenetic analysis.Analyses inferred from the sequences of internal transcribed spacer region(ITS1,5.8S nrDNA and ITS2),partial translation elongation factor 1-alpha(TEF1)and partial actin gene(ACT),showed these representative Phyllosticta isolates clustered in four distinct clades corresponding to three known,and one undescribed species.The newly resolved taxon,Phyllosticta citrichinaensis was isolated from leaves and fruits of all four Citrus species and is introduced in this paper.This taxon caused minor damage,showing irregular spots or freckles.Phyllosticta citriasiana,associated with tan spot of pomeloes,was isolated only from pomeloes,and never from lemons,mandarins and oranges.Phyllosticta citricarpa,the citrus black spot pathogen,which is presently subjected to phytosanitary legislation in the EU and United States,was isolated from lemons,mandarins and oranges,but never from pomeloes.The isolates of P.citricarpa clustered in two subclades,one from mandarins,the other from oranges and lemons.P.capitalensis was isolated from all four Citrus species as an endophyte,causing false melanose,or together with P.citricarpa or P.citriasiana.Morphological,cultural and biochemical characters were consistent with the results of phylogenetic analysis.In addition,a specific primer pair Pca8/ITS4 was designed and selected,and its corresponding PCR procedure was developed for the detection of P.citriasiana in this study.

Combining simplified DNA extraction technology and recombinase polymerase amplification assay for rapid and equipment-free detection of citrus pathogen Phytophthora parasitica

Foot and root rot caused by Phytophthora parasitica is a substantial threat to citrus cultivation,affecting both yield and quality.Thus,rapid and accurate detection of P.parasitica plays an important role in disease management.The aim of this study was to develop a simple diagnostic method to detect P.parasitica infection by combining recombinase polymerase amplification and lateral flow strips(LF-RPA).To establish the LF-RPA assay of P.parasitica,the primers and probe designed based on the Ypt1 gene were tested for specificity to P.parasitica,which showed no cross-reactivity with DNAs of other related oomycete species.The LF-RPA assay detected the amount of genomic DNA of P.parasitica which was as low as 1 pg.To make the LF-RPA assay useful in low-resource settings,four simplified DNA extraction methods were compared,after which the LF-RPA assay was applied,with no specialized equipment,to analyze a diverse range of citrus tissues by using a simplified PEG-NaOH method for DNA extraction.This method was successful in detecting P.parasitica in infected plant samples within 30 min.Combining the LF-RPA assay and a simplified DNA extraction method could be a potential detection test for P.parasitica,especially in areas with limited resources.

Colletotrichum species associated with cultivated citrus in China

There have been considerable advances in the understanding of species concepts in the genus Colletotrichum.This has lead to the need to carry out fresh surveys of Colletotrichum species associated with important hosts.Colletotrichum species are associated with Citrus plants as saprobes,important pre-harvest and post-harvest pathogens,as well as endophytes.In this study,a total of 312 Colletotrichum strains were isolated from leaves,shoots and fruits of cultivated Citrus and Fortunella species with or without disease symptoms across the main citrus production areas in China.The morphology of all strains were studied and multilocus(ACT,TUB2,CAL,GAPDH,GS,ITS)phylogeny established.Strains were from four important species complexes of Colletotrichum,namely C.gloeosporioides species complex,C.boninense species complex,C.acutatum species complex and a final group including C.truncatum,which was rare on Citrus species.The species belonging to the C.gloeosporioides species complex comprised C.gloeos porioides and C.fructicola,the C.boninense complex comprised C.karstii and a new species C.citricola and the C.acutatum complex included a new species,C.citri.The ability of strains to cause anthracnose on citrus fruits was tested by inoculation and strains of Colletotrichum gloeosporioides,C.fructicola and C.truncatum were pathogenic.