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.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

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.

Chemical Application for Prevention and Control of Post-harvest Citrus Diseases

[ Objective] To study the control efficiency of 45% Prochloraz amines ME and 500 g/L Triabendazole SC for the post-harvest diseases of citrus fruits, and the economic and efficient dosage and application techniques. [ Method] Carry out prevention and control test of common post-harvest diseases of tangerines, such as anthracnose, green mold and penicillium disease. [ Result] The experiment agent 45% Prochloraz amines ME showed excellent effect in controlling post- harvest anthracnose of citrus fruits, the 45t^-day control efficiency was above 73% ; the 45ts-day control efficiency of 45% Prochloras amines ME and 500 g/L Tri- abendazole SC for green mold and penicillium disease was above 72%. [ Conclusion] 45% Prochloraz amines ME and 500 g/L Triabendazole SC are two ideal agents for preventing and controlling post-harvest diseases and safeguarding quality of citrus fruits.

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.

Identification and Control of HLB Disease in Citrus grandis

HLB disease has been endangering citrus production,and it is an important task in Citrus grandis production to identify and control HLB disease. Diaphorina citri and Trioza erytreae are the main vectors for the spreading of HLB disease. Scientific and proper release of predatory natural enemies such as ladybugs,combined with chemical control can effectively control psyllid. For suspected HLB disease strains,a simple " HLB disease detection reagent" can be used for detection. This method is simple,cheap and accurate,and it is an economical and feasible identification method for ordinary growers.

Discrimination of Five Citrus Diseased Leaves by FTIR Spectroscopy

In this paper,citrus brown spot,huanglongbing,canker,fuliginous,Cercospora sp. and healthy leaves were studied using Fourier transform infrared spectroscopy( FTIR) combined with statistical analysis. The results showed that the spectra of the samples were similar,whereas there were obvious differences in the second derivatives of infrared spectra in the range of 1 500- 700 cm- 1. The correlative analysis were evaluated,results showed that the correlation coefficients were larger than 0. 918 between healthy leaves,and between the same diseased leaves. However,the values were all decreased between healthy and diseased leaves,and among different diseased leaves. The preprocessed original,first derivative and second derivative spectra in the range of 1 200- 700 cm- 1were chosen to evaluated principal component analysis( PCA) and hierarchical cluster analysis( HCA),respectively. The performance of the overall accuracy of PCA was 92.5%,which were better than original dataset and first derivative dataset. HCA by selecting second derivative dataset yield about 90% accuracy. This study proved that FTIR spectroscopy could be detected citrus diseases quickly and accurately.

Improvement of Detection Methods and Further Characterization of <i>Spiroplasma citri</i>, the Causal Agent of Citrus Stubborn Disease in Egypt

Stubborn disease of citrus is one of the main causes of quality deterioration of citrus fruits in Egypt. The early detection and the molecular characterization of the causal agent are vital for revealing its real distribution and for management. In 2011, several samples were collected at different times of the year from stubborn suspected symptomatic trees within the main citrus growing area in Egypt, the Nile-delta region. After culturing the causal agent on artificial LD8 media from the field fresh samples, two new and improved methods of biological indexing were set up and compared with the traditional method in order to increase the detection efficiency by increasing the greenhouse transmission rate;which reached 85% with the new inverse inoculation method. Different PCR primer pairs were evaluated for their detection efficiency of the Egyptian Isolates of Spiroplasma citri and the most specific primer pair for these local isolates was determined. Improving the efficiency of biological indexing, along with determining the most specific and efficient PCR primer pair for the detection, will enhance and facilitate the citrus certification programs in Egypt, making them better tools for the early detection of stubborn disease. Furthermore obtained Egyptian isolates were characterized molecularly by the analysis of the obtained sequences showing close relationship with the Moroccan strain (GII3).

Spatial Organisation Influences Citrus <i>Pseudocercospora</i>Leaf and Fruit Spot Disease Severity in Cocoa-Based Agroforestry Systems

An investigation to assess the spatial structure and severity of Pseudocercospora leaf and fruit spot disease (PLFSD) on citrus trees in cocoa-based agroforests was carried out in three contrasting ecological zones in southern Cameroon, viz: 1) the humid forest zone, 2) the degraded forest zone, and 3) the forest-savannah transition zone. Two main parameters were investigated viz: 1) the spatial structure of cocoa based agroforests, and 2) the disease severity. In total, the spatial structure of 19 cocoa-based agroforests was analysed using the Ripley K(r) function, meanwhile the collection of epidemiological data that consisted of noting the presence of PLFSD spots on leaves and fruits on 438 citrus trees was used to characterise the severity of the disease. Results showed that, the spatial structure of citrus trees in these agroforests investigated were regular in seven plots, random in nine, and aggregated in three. Aggregated plots presented a significantly higher mean of disease severity on leaves and fruits (28.55 and 30.37 respectively), as compared to randomised (20.91 and 16.32 respectively) and regular plots (16.28 and 14.97 respectively), at P-value < 0.05. These results suggest that the spatial structure of citrus trees in the cocoa-based agroforests studied influences the severity of PFLSD. Proper integrated control measures can therefore be initiated, leading to a considerable reduction of the use of manufactured inputs, and thereby, the cost of production of citrus fruits.

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.

A Rapid Identification Method for Germplasm Materials Resistant to Citrus Huanglongbing Disease

[Objectives]This study was conducted to develop a rapid identification method for citrus germline materials resistant to Huanglongbing disease and lay a basis for accelerating citrus breeding for resistance to Huanglongbing and increasing the breeding efficiency.[Methods]Thirty-six citrus germplasms suspected to be resistant to citrus Huanglongbing disease were collected.The method of direct high grafting to citrus trees infected with Huanglongbing pathogen was adopted.The resistance of the test materials was identified and evaluated by field symptoms combined with quantitative PCR.It was defined as the top grafting identification method.[Results]The test materials that were grafted in spring started to germinate after one month,and three months late(June 5,2018)typical mottled yellowing on leaves was observed on KH-14 for the first time.After four months(July 5,2018)of top grafting,typical mottled yellowing occurred on 23 materials,and 11 materials showed no such symptom.After six months(September 4,2018)of top grafting,although the growth of KH-18,KH-12,KHY-4,KHY-5 and KHY-6 were normal,yellowing was observed on their leaves.Only KH-21 grew well,and showed no yellow shoots and yellowing leaves.It was identified as the material with resistance to Huanglongbing disease.Quantitative PCR tests on the above six materials showed that KH-21 was negative and other five were positive.Real-time fluorescence quantitative PCR test indicated that the average Huanglongbing bacteria amount in KH-21 was 1870.0 cell/μg DNA,and the average Huanglongbing bacteria amount in the control material was 372285.5 cell/μg DNA,indicating KH-21 was resistant to Huanglongbing bacteria.[Conclusions]The method for infecting bacteria by top grafting takes six months,can detect large amount of seedlings,and is time-saving,efficient,cost-saving and accurate.This method can quickly identify the resistance of citrus varieties to citrus Huanglongbing disease,and can be popularized and used in the identification of citrus Huanglongbing disease resistance.

柑橘黑斑病(Citrus Black Spot)发生危害现状和研究进展

柑橘黑斑病(Citrus Black Spot,CBS)主要危害果实,在果皮上形成各种病斑而影响果实的外观品质和销售价格,严重时可造成田间落果和贮藏期果实腐烂。同时,黑斑病被欧盟列为A1类外来有害生物,美国对此也有严格的限制。因此,该病害影响柑橘鲜果的国际贸易。黑斑病的病原属球座菌(Guignardia),其无性态为叶点霉菌(Phyl-losticta)。宽皮柑橘(Citrus reticulata)、橙类(C.sinensis)和柠檬(C.limon)上为柑橘球座菌(G.citricarpa),无性态为柑橘叶点霉(P.citricarpa)。而最新研究表明亚洲柚黑斑病(也称tan spot,棕褐斑病)的病原与上述柑橘上的不同,其有性态尚未发现,无性态则为亚洲柑橘叶点霉菌(P.citriasiana)。此外,P.capitalensis作为一种内生菌普遍存在于各类柑橘中,常干扰病害的检测。鉴于该病害在我国发生普遍,局部危害严重,而相关信息匮乏,作者综合国内外相关研究报道,结合自身部分工作,就病害发生历史、分布和危害性,病害症状,病原菌种类,发生规律和检测,病害在欧盟国家的发生风险,以及病害的防治等方面作了较为详细的介绍,以期满足读者对柑橘黑斑病的全方位了解需求。

Expressing p20 hairpin RNA of Citrus tristeza virus confers Citrus aurantium with tolerance/resistance against stem pitting and seedling yellow CTV strains

The Citrus tristeza virus (CTV) uses 3 silencing suppressor genes, p20, p23 and p25, to resist the attacks from its Citrus hosts. Inactivating these genes is therefore obviously a potential defensive option in addition to the current control strat-egies including aphid management and the use of mild strain cross protection. In this study, we cloned partial DNA frag-ments from the three genes, and used them to construct vectors for expressing hairpin RNAs (hpRNAs). To facilitate the formation of hpRNAs, the constructs were introduced in a loop structure. Fol owing transformation of sour orange (Citrus aurantium) with these constructs, 8 p20 hpRNA (hp20) and 1 p25 hpRNA (hp25) expressing lines were obtained. The 7 hp20 transgenic lines were further characterized. Their reactions to CTV were tested fol owing inoculation with CT14A and/or TR-L514, both of which are severe strains. Results showed that 3 lines (hp20-5, hp20-6 and hp20-8) were completely resistant to TR-L514 under greenhouse conditions for no detectable viral load was found in their leaves by PCR. However, they exhibited only partial suppression of TR-L514 under screen house conditions since the virus was detected in their leaves, though 2 months later compared to non-transgenic controls. Further tests showed that hp20-5 was tolerant also to CT14A under screen house conditions. The growth of hp20-5 was much better than others including the controls that were concurrently chal enged with CT14A. These results showed that expressing p20 hpRNA was sufifcient to confer sour orange with CTV resistance/tolerance.

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.

Epsilon-poly-L-lysine increases disease resistance of citrus against postharvest green mold by activating amino acid metabolism and phenolic comnpounds biosynthesis

As a famous fruit worldwide,citrus is susceptible to green mold caused by Penicillium digitatum,which causes large economic losses every year.e-Poly-L-lysine(e-PL)is a novel preservative with strong inhibitory effects on fungi,and has the capacity to induce disease resistance in fruit,but the mechanism has been reported rarely,especially in citrus.In the present study,8ooμg/mL e-PL and P digitatum spores were inoculated in two different wounds on the citrus pericarp at an interval of 24 h.The results revealed that e-PL inhibited that the development of green mold without direct contact with P digitatum,indicating that the disease resistance of citrus was activated.Transcriptome analysis revealed that e-PL activated amino acid metabolism and phenylpropanoid biosynthesis.Besides,the accumulation of glutamic acid,proline,arginine,serine,lysine,phenylalanine,and tyrosine were changed during storage.In phenylpropanoid biosynthesis,-PL increased phenylalanine ammonia-lyase(PAL),cinnamate 4-hydroxylase(C4H),and 4-coumarate:coenzyme A ligase(4CL)activities and total phenolic and flavonoid contents.Importantly.among these phenolic compounds,e-PL promoted the accumulation of individual phenolic compounds including ferulic acid,chlorogenic acid,p-coumaric acid,caffeic acid,gallic acid,catechins,epicatechin,and narirutin.In conclusion,e-PL enhanced the resistance of citrus through amino acid metabolism and accumulation of phenolic compounds.These results improved the knowledge of the mechanism of-PL-induced disease resistance and provided a fresh theoretical basis for the use of e-PL in postharvest citrus preservation.

基于改进SSD模型的柑橘叶片病害轻量化检测模型

针对当前目标检测算法存在模型占比大,对柑橘叶片病害检测速度较慢、精度较低等问题,提出了一种基于改进SSD(single shot multibox detector)的柑橘叶片病害轻量化检测方法。引入了轻量化卷积神经网络MobileNetV2作为SSD网络的骨架,以减小模型规模、提高检测速度。引入感受野模块(receptive field block,RFB)来扩大浅层特征感受野,以提高模型对小目标的检测效果。并引入CA(coordinate attention)注意力机制,以强化不同深度的特征信息,进一步提升柑橘叶片病害的识别精度。结果表明,与VGG16-SSD相比,改进模型(MR-CA-SSD)在柑橘叶片病害检测上平均精度均值(mAP)提升4.4百分点,模型占比减小52.3 MB,每秒检测帧数提升3.15。MR-CA-SSD综合性能也优于YOLOv4、CenterNet、Efficientnet-YoloV3等模型。该改进模型可实现对柑橘叶片病害的快速准确诊断,有助于对病害部位及时精准施药。

柑橘黑点病研究进展

柑橘黑点病是由柑橘间座壳菌(Diaporthe citri)引起的一种重要柑橘病害,在全球柑橘主产区均有发生,主要危害叶片、枝梢和果实,严重影响柑橘的品质和鲜销商品价值。柑橘间座壳菌可以侵染所有当前栽培的柑橘品种,其中以葡萄柚和柠檬最易感病。喷施代森锰锌和铜制剂等杀菌剂是防控柑橘黑点病的重要方法,但柑橘间座壳菌在自然条件下有性生殖频繁,具有丰富的遗传多样性,需加强监测柑橘间座壳菌种群对代森锰锌等药剂的敏感性变化。近年来,关于柑橘间座壳菌的基因组信息、快速检测技术、遗传分化、致病机制以及防治方法等方面的研究取得较为显著的进展。就国内外近年来柑橘黑点病的危害症状与分布、病原种类、遗传多样性、生物学特性、侵染过程、致病机制、发生规律以及防治方法等方面的最新研究进展进行综述,并对柑橘黑点病的未来重点研究方向进行展望,以期为柑橘黑点病的防控策略提供科学依据。

从江柑橘黑点病发生调查及其防治药剂筛选

【目的】探明从江柑橘黑点病发生情况及规律,并筛选出有效防治药剂,为从江柑橘黑点病的防控提供参考。【方法】采用病情普查及系统调查方法,开展从江柑橘黑点病发生情况与发病规律调查;通过在田间设置不同药剂及施药方式处理,开展黑点病防控药剂筛选。【结果】调查果园的柑橘黑点病发病率为100%,病情指数大于50的果园占40%,老果园发病重,不同品种均可感病,夏季雨季及9月上旬是防治关键时期。以80%代森锰锌700倍液、70%丙森锌可湿性粉剂1000倍液、50%氟啶胺悬浮液1000倍液3种药剂按次序轮换使用2次以及50%氟啶胺悬浮液1000倍液+绿颖矿物油200倍液2种处理方式对柑橘黑点病的田间防效最佳,防效达100%;其次为50%氟啶胺悬浮液1000倍液,防效达92.31%。【结论】从江柑橘黑点病发病率高,其中以老果园发病较重,防治关键时期为夏季雨季及9月上旬。从江柑橘黑点病防治推荐80%代森锰锌可湿性粉剂700倍液、70%丙森锌可湿性粉剂1000倍液、50%氟啶胺悬浮液1000倍液3种药剂按次序轮换使用2次,或用50%氟啶胺悬浮液1000倍液配合绿颖矿物油200倍液施用,可取得明显的防治效果。

基于改进ShuffleNet V2的柑橘病害识别研究

大型卷积神经网络因模型复杂难以部署于实际应用,而轻量级网络因优化模型结构而导致精度往往不如前者理想。针对上述问题,对ShuffleNet V2进行改进,提出一种轻量化MAM-ShuffleNet柑橘病害识别模型。首先,在ShuffleNet V2中引入混合注意力模块(Mixed attention module,MAM),提升模型对病害特征提取能力。其次,利用Ghost模块优化网络中卷积层,有效降低网络模型参数量和计算成本。最后,调整网络结构中ShuffleNet V2单元堆叠次数,进一步简化网络参数。结果表明,在自建柑橘叶片数据集中,MAM-ShuffleNet模型平均识别准确率达到97.7%;与原始ShuffleNet V2相比,其参数量降低了45.7%,识别准确率提升了1.2百分点;综合性能明显优于ResNet50、DenseNet121等模型。