Showing papers on "Leaf spot published in 2022"

GAN based image augmentation for increased CNN performance in Paddy leaf disease classification

Abstract: A variety of fungal and bacterial leaf ailments wreak havoc on the paddy plant in the agricultural field. Early diagnosis of leaf infection can improve the yield of the crop. The modeling of an automatic disease classifier aids farmers in handling the spread of leaf disease in the agricultural field. This work cogitates three paddy leaf diseases (Bacterial blight, leaf smut, and leaf blast) for the creation of an AI-based robust detection and classification model. The dataset is collected from a variety of standard online repositories. GAN-based augmentation technique was used for increasing the size of the dataset. A novel approach to the convolutional neural network is proposed with the combination of augmentation and a CNN model tuner. The performance of CNN is evaluated in terms of accuracy achieved is 98.23\% in the classification process.

Beans Leaf Diseases Classification Using MobileNet Models

Abstract: In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, beans is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, beans are a great source of protein that offer many health benefits, but there are a lot of diseases associated with beans leaf which hinder its production such as angular leaf spot disease and bean rust disease. Thus, an accurate classification of bean leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify beans leaf disease by using public dataset of leaf image and MobileNet model with the open source library TensorFlow. In this study, we proposed a method to classify beans leaf disease and to find and describe the efficient network architecture (hyperparameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying bean leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using MobileNetV2 architecture under the some controlled conditions as MobileNet to check if we could get faster training times, higher accuracy and easier retraining, we evaluated and implemented MobileNet architectures on one public dataset including two unhealthy classes (angular leaf spot disease and bean rust disease) and one healthy class, the algorithm was tested on 1296 images of bean leaf. The obtained results showed that our MobileNet model achieves high classification performance for beans leaf disease, the classification average accuracy of the proposed model is more than 97% on training dataset and more than 92% on test data for two unhealthy classes and one healthy class.

Maize Small Leaf Spot Classification Based on Improved Deep Convolutional Neural Networks with a Multi-Scale Attention Mechanism

Abstract: Maize small leaf spot (Bipolaris maydis) is one of the most important diseases of maize. The severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is polluted. Therefore, in order to solve this problem, this study proposes a novel deep learning network DISE-Net. We designed a dilated-inception module instead of the traditional inception module for strengthening the performance of multi-scale feature extraction, then embedded the attention module to learn the importance of interchannel relationships for input features. In addition, a dense connection strategy is used in model building to strengthen channel feature propagation. In this paper, we constructed a data set of maize small leaf spot, including 1268 images of four disease grades and healthy leaves. Comparative experiments show that DISE-Net with a test accuracy of 97.12% outperforms the classical VGG16 (91.11%), ResNet50 (89.77%), InceptionV3 (90.97%), MobileNetv1 (92.51%), MobileNetv2 (92.17%) and DenseNet121 (94.25%). In addition, Grad-Cam network visualization also shows that DISE-Net is able to pay more attention to the key areas in making the decision. The results showed that the DISE-Net was suitable for the classification of maize small leaf spot in the field.

<i>Alternaria</i> spp. Associated with Leaf Blight of Maize in Heilongjiang Province, China

Abstract: Maize (Zea mays L.) is a major economic crop worldwide. Maize can be infected by Alternaria species causing leaf blight that can result in significant economic losses. In this study, 168 Alternaria isolates recovered from symptomatic maize leaves were identified based on morphological characteristics, pathogenicity, and multilocus sequence analyses of the genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the internal transcribed spacer of ribosomal DNA (rDNA ITS), the RNA polymerase II second largest subunit (RPB2), and histone3 (HIS3). Maize isolates grouped to four Alternaria species including Alternaria tenuissima, A. alternata, A. burnsii, and Alternaria sp. Notably, A. tenuissima (71.4%) was the most prevalent of the four isolated species, followed by A. alternata (21.5%), Alternaria sp. (4.1%), and A. burnsii (3.0%). Pathogenicity tests showed that all four Alternaria species could produce elliptic to nearly round, or strip, lesions on leaves of maize, gray-white to dry white in the lesion centers and reddish-brown at the edges. The average disease incidence (58.47%) and average disease index (63.55) of maize leaves inoculated with A. alternata were significantly higher than levels resulting from A. tenuissima (55.28% and 58.49), Alternaria sp. (55.34% and 58.75), and A. burnsii (56% and 55). Haplotype analyses indicated that there were 14 haplotypes of A. tenuissima and five haplotypes of A. alternata in Heilongjiang Province and suggested the occurrence of a population expansion. Results of the study showed that Alternaria species associated with maize leaf blight in Heilongjiang Province are more diverse than those that have been previously reported. This is the first report globally of A. tenuissima, A. burnsii, and an unclassified Alternaria species as causal agents of leaf blight on maize.

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Abstract: Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.

Chitosan as an Adjuvant to Improve Isopyrazam Azoxystrobin against Leaf Spot Disease of Kiwifruit and Enhance Its Photosynthesis, Quality, and Amino Acids

Abstract: Leaf spot disease caused by Lasiodiplodia theobromae is one of the most serious fungal diseases of kiwifruit production. In this work, the co-application of isopyrazam·azoxystrobin and chitosan against leaf spot disease in kiwifruit and its effects on disease resistance, photosynthesis, yield, quality, and amino acids of kiwifruit were investigated. The results show that isopyrazam·azoxystrobin exhibited a superior bioactivity against L. theobromae with an EC50 value of 0.1826 mg kg−1. The foliar application of chitosan could effectively enhance isopyrazam·azoxystrobin against leaf spot disease with a field control efficacy of 86.83% by spraying 29% isopyrazam·azoxystrobin suspension concentrate (SC) 1500 time + chitosan 100-time liquid, which was significantly (p < 0.05) higher than 78.70% of 29% isopyrazam·azoxystrobin SC 1000-time liquid. The co-application of isopyrazam·azoxystrobin and chitosan effectively enhanced soluble protein, resistance enzymes’ activity in kiwifruit leaves, and reduced their malonaldehyde (MDA), as well as reliably improved their photosynthetic characteristics. Simultaneously, their co-application was more effective in promoting growth, quality, and amino acids of kiwifruit fruits compared to isopyrazam·azoxystrobin or chitosan alone. This study highlights that the co-application of isopyrazam·azoxystrobin and chitosan can be used as a green, safe, and efficient approach for controlling leaf spot disease of kiwifruit and reducing the application of chemical fungicides.

Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height

Abstract: Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height. A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.

Detection of rice plant disease using adaboostsvm classifier

Abstract: The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

Diversity of Colletotrichum Species Causing Apple Bitter Rot and Glomerella Leaf Spot in China

Abstract: Bitter rot and Glomerella leaf spot (GLS) of apples, caused by Colletotrichum species, are major diseases of apples around the world. A total of 98 isolates were obtained from apple fruits with bitter rot, and 53 isolates were obtained from leaves with leaf spot in the primary apple production regions in China. These isolates were characterized morphologically, and five gene regions (ITS, ACT, GAPDH, CHS-1 and TUB2) were sequenced for each isolate. A phylogenetic analysis, combined with a comparison of the morphological, cultural and pathogenic characters, sorted bitter rot isolates into six species: C. alienum, C. fructicola, C. gloeosporioides sensu stricto, C. nymphaeae, C. siamense and one new species, C. orientalis Dandan Fu & G.Y. Sun. Among these, C. siamense was the predominant pathogen associated with bitter rot. Isolates from leaf spot were identified as two species, C. aenigma and C. fructicola. This is the first report of C. orientalis as an apple bitter rot pathogen worldwide, and the results provide important insights into the diversity of Colletotrichum species in China.

Efficacy of Chitosan Nanoparticle Loaded-Salicylic Acid and -Silver on Management of Cassava Leaf Spot Disease

Abstract: Leaf spot is one of the most important cassava diseases. Nanotechnology can be applied to control diseases and improve plant growth. This study was performed to prepare chitosan (CS) nanoparticle (NP)-loaded salicylic acid (SA) or silver (Ag) by the ionic gelation method, and to evaluate their effectiveness on reducing leaf spot disease and enhancing the growth of cassava plants. The CS (0.4 or 0.5%) and Pentasodium triphosphate (0.2 or 0.5%) were mixed with SA varying at 0.05, 0.1, or 0.2% or silver nitrate varying at 1, 2, or 3 mM to prepare three formulations of CS-NP-loaded SA named N1, N2, and N3 or CS-NP-loaded Ag named N4, N5, and N6. The results showed that the six formulations were not toxic to cassava leaves up to 800 ppm. The CS-NP-loaded SA (N3) and CS-NP-loaded Ag (N6) were more effective than the remaining formulations in reducing the disease severity and the disease index of leaf spot. Furthermore, N3 at 400 ppm and N6 at 200, 400, and 800 ppm could reduce disease severity (68.9–73.6% or 37.0–37.7%, depending on the time of treatment and the pathogen density) and enhance plant growth more than or equal to commercial fungicide or nano-fungicide products under net-house conditions. The study indicates the potential to use CS-NP-loaded SA or Ag as elicitors to manage cassava leaf spot disease.

A Comprehensive Overview of the Genes and Functions Required for Lettuce Infection by the Hemibiotrophic Phytopathogen Xanthomonas hortorum pv. vitians

Abstract: Xanthomonas hortorum was recently the subject of renewed interest, as several studies highlighted that its members were responsible for diseases in a wide range of plant species, including crops of agricultural relevance (e.g., tomato and carrot). Among X. hortorum variants, X. hortorum pv. vitians is a reemerging foliar hemibiotrophic phytopathogen responsible for severe outbreaks of bacterial leaf spot of lettuce all around the world. ABSTRACT The successful infection of a host plant by a phytopathogenic bacterium depends on a finely tuned molecular cross talk between the two partners. Thanks to transposon insertion sequencing techniques (Tn-seq), whole genomes can now be assessed to determine which genes are important for the fitness of several plant-associated bacteria in planta. Despite its agricultural relevance, the dynamic molecular interaction established between the foliar hemibiotrophic phytopathogen Xanthomonas hortorum pv. vitians and its host, lettuce (Lactuca sativa), remains completely unknown. To decipher the genes and functions mobilized by the pathogen throughout the infection process, we conducted a Tn-seq experiment in lettuce leaves to mimic the selective pressure occurring during natural infection. This genome-wide screening identified 170 genes whose disruption caused serious fitness defects in lettuce. A thorough examination of these genes using comparative genomics and gene set enrichment analyses highlighted that several functions and pathways were highly critical for the pathogen’s survival. Numerous genes involved in amino acid, nucleic acid, and exopolysaccharide biosynthesis were critical. The xps type II secretion system operon, a few TonB-dependent transporters involved in carbohydrate or siderophore scavenging, and multiple genes of the carbohydrate catabolism pathways were also critical, emphasizing the importance of nutrition systems in a nutrient-limited environment. Finally, several genes implied in camouflage from the plant immune system and resistance to immunity-induced oxidative stress were strongly involved in host colonization. As a whole, these results highlight some of the central metabolic pathways and cellular functions critical for Xanthomonas host adaptation and pathogenesis. IMPORTANCE Xanthomonas hortorum was recently the subject of renewed interest, as several studies highlighted that its members were responsible for diseases in a wide range of plant species, including crops of agricultural relevance (e.g., tomato and carrot). Among X. hortorum variants, X. hortorum pv. vitians is a reemerging foliar hemibiotrophic phytopathogen responsible for severe outbreaks of bacterial leaf spot of lettuce all around the world. Despite recent findings, sustainable and practical means of disease control remain to be developed. Understanding the host-pathogen interaction from a molecular perspective is crucial to support these efforts. The genes and functions mobilized by X. hortorum pv. vitians during its interaction with lettuce had never been investigated. Our study sheds light on these processes by screening the whole pathogen genome for genes critical for its fitness during the infection process, using transposon insertion sequencing and comparative genomics.

Colletotrichum Spp. Diversity Between Leaf Anthracnose and Crown Rot From the Same Strawberry Plant

Abstract: Leaf anthracnose (LA) and anthracnose crown rot (ACR) represent serious fungal diseases that pose significant threats to strawberry production. To characterize the pathogen diversity associated with above diseases, 100 strawberry plants, including varieties of “Hongjia,” “Zhangji,” and “Tianxianzui,” were sampled from Jiande and Zhoushan, the primary plantation regions of Zhejiang province, China. A total of 309 Colletotrichum isolates were isolated from crown (150 isolates) and leaves (159 isolates) of affected samples. Among these, 100 isolates obtained from the plants showing both LA and CR symptoms were selected randomly for further characterization. Based on the morphological observations combined with phylogenetic analysis of multiple genes (ACT, ITS, CAL, GAPDH, and CHS), all the 100 tested isolates were identified as C. gloeosporioides species complex, including 91 isolates of C. siamense, 8 isolates of C. fructicola causing both LA and ACR, and one isolate of C. aenigma causing ACR. The phenotypic characteristics of these isolated species were investigated using the BIOLOG phenotype MicroArray (PM) and a total of 950 different metabolic phenotype were tested, showing the characteristics among these isolates and providing the theoretical basis for pathogenic biochemistry and metabolism. The pathogenicity tests showed that even the same Colletotrichum species isolated from different diseased tissues (leaves or crowns) had significantly different pathogenicity toward strawberry leaves and crown. C. siamense isolated from diseased leaves (CSLA) was more aggressive than C. siamense isolated from rotted crown (CSCR) during the infection on “Zhangji” leaves. Additionally, C. fructicola isolated from affected leaf (CFLA) caused more severe symptoms on the leaves of four strawberry varieties compared to C. fructicola isolated from diseased crown (CFCR). For crown rot, the pathogenicity of CSCR was higher than that of CSLA.

First Report of <i>Epicoccum nigrum</i> Causing Brown Leaf Spot in Tea in Guizhou Province, China

Abstract: HomePlant DiseaseVol. 106, No. 1First Report of Epicoccum nigrum Causing Brown Leaf Spot in Tea in Guizhou Province, China PreviousNext DISEASE NOTE OPENOpen Access licenseFirst Report of Epicoccum nigrum Causing Brown Leaf Spot in Tea in Guizhou Province, ChinaQ. X. Yin, S. L. Jiang, D. X. Li, H. L. Huang, Y. Wang, D. L. Wang, and Z. ChenQ. X. YinKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, S. L. JiangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, D. X. LiKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, H. L. HuangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, Y. Wanghttps://orcid.org/0000-0003-3831-2117Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, D. L. Wang†Corresponding authors: D. L. Wang; E-mail Address: dlwang@gzu.edu.cn, and Z. Chen; E-mail Address: gychenzhuo@aliyun.comKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author, and Z. Chen†Corresponding authors: D. L. Wang; E-mail Address: dlwang@gzu.edu.cn, and Z. Chen; E-mail Address: gychenzhuo@aliyun.comhttps://orcid.org/0000-0001-7130-8457Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, ChinaSearch for more papers by this author AffiliationsAuthors and Affiliations Q. X. Yin S. L. Jiang D. X. Li H. L. Huang Y. Wang D. L. Wang † Z. Chen † Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou, China Published Online:8 Jan 2022https://doi.org/10.1094/PDIS-04-21-0815-PDNAboutSectionsView articlePDFSupplemental ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat View articleBrown leaf spots were observed on tea (Camellia sinensis [L. O.] Kuntze) in Sinan County (27.74°N, 108.35°E) and Kaiyang County (27.96°N, 107.34°E), Guizhou Province, China, from 2018 to 2020. For leaf spots with typical symptoms, the disease incidence was estimated to range between 56 and 61%, respectively. The disease severity was estimated to range from 39 to 43 across 12 tea plantations, respectively. The disease initially occurred at the margins of leaf tips, and the lesions expanded gradually, being dark brown and irregularly shaped and becoming necrotic. To identify the causal organism, two leaves from each of 15 tea twigs, one or two per plantation, were detached from 8- or 10-year-old tea plants on each of 12 plantations. Samples taken from the lesion margins were sterilized with 75% ethanol followed by 0.5% NaOCl, placed on potato dextrose agar (PDA), and then incubated at 25°C in darkness for 5 days (Wang et al. 2020). For each sample, hyphal tips from the margin of a growing colony were successively transferred to fresh PDA, and pure cultures were obtained. Three representative strains were grown on PDA, malt extract agar (MEA), and oatmeal agar (OA) plates. The colonies had smooth margins and abundant mycelia on all three media, with the colony colors being from gray to light purple on PDA, white on MEA, and purplish-red on OA at 5 days postinoculation. At 20 days postinoculation on MEA, stromata began to gradually form, which were droplet-like, 100 to 2,000 μm in diameter, and semi-immersed on the medium’s surface. Black sporodochia were produced on the surfaces of stromata. Conidiophores were aggregated in sporodochia, densely compacted, and dark brown. Conidia were globose or pyriform, dark, multicellular, and measured 22.95 ± 3.59 × 19.82 ± 3.13 μm (n = 50) in diameter. The morphological characteristics of the mycelia and reproductive structures of the strains were identical to those of Epicoccum nigrum (Sheikhloo et al. 2011). The internal transcribed spacer (ITS) region of rDNA, and the partial 28S large subunit rDNA (LSU), RNA polymerase II second largest subunit (RPB2), and beta-tubulin (TUB) genes of these strains were amplified using the primers V9G/ITS4 (De Hoog and Gerrits van den Ende 1998; White et al. 1990), LR0R/LR5 (Rehner and Samuels 1994), RPB2-5F2/fRPB2-7cR (Sung et al. 2007), and TUB2Fd/TUB4Rd (Woudenberg et al. 2009), respectively, and deposited in GenBank (accession nos. MW646378, MW291537, MW602293, and MW602295 for ITS, LSU, RBP2, and TUB, respectively). A maximum parsimony phylogenetic analysis indicated that the representative strains clustered with E. nigrum CBS 173.73 (Chen et al. 2017). Pathogenicity tests were performed on 5-year-old potted tea and on 10-year-old C. sinensis cv. Fuding-dabaicha in the field. Mycelial plugs (6-mm diameter) and a conidial suspension (106 conidial/ml) were applied on punctured leaves using a sterile needle and nonpunctured leaves. Inoculation with only a PDA plug or sterile water served as controls. Brown spots appeared on the wounded sites of tea leaves at 2 days postinoculation. No symptoms were observed on the nonwounded leaves or wounded leaves inoculated with PDA plugs lacking mycelia. The reisolated pathogen from diseased plants was identical to the purified strain ACCC39731 used for inoculation, with reisolation frequency being 85.0%. To our knowledge, this is the first report of E. nigrum causing leaf spot on tea plants in China, and our findings will be useful for its management and further research.The author(s) declare no conflict of interest.References:Chen, Q., et al. 2017. Stud. Mycol. 87:105. https://doi.org/10.1016/j.simyco.2017.06.002 Crossref, ISI, Google ScholarDe Hoog, G. S., and Gerrits van den Ende, A. H. 1998. Mycoses 41:183. https://doi.org/10.1111/j.1439-0507.1998.tb00321.x Crossref, ISI, Google ScholarRehner, S. A., and Samuels, G. J. 1994. Mycol. Res. 98:625. Google ScholarSheikhloo, Z., et al. 2011. J. Clust Sci. 22:661. Google ScholarSung, G. H., et al. 2007. Mol. Phylogenet. Evol. 44:1204. https://doi.org/10.1016/j.ympev.2007.03.011 Crossref, ISI, Google ScholarWang, X., et al. 2020. Plant Dis. 104:1254. https://doi.org/10.1094/PDIS-08-19-1782-PDN Link, ISI, Google ScholarWhite, T. J., et al. 1990. Page 315 in: PCR Protocols: A Guide to Methods and Applications. Academic Press, San Diego, CA. Google ScholarWoudenberg, J. H. C., et al. 2009. Persoonia 22:56. https://doi.org/10.3767/003158509X427808 Crossref, ISI, Google ScholarFunding: This research was funded by National Key Research Development Program of China (2017YFD0200308), and its Post-subsidy project ([2018]5262), the National Natural Science Foundation of China (no. 21977023, no. 31860515), and Program of Introducing Talents to Chinese Universities (111 Program, D20023).The author(s) declare no conflict of interest.DetailsFiguresLiterature CitedRelated Vol. 106, No. 1 January 2022SubscribeISSN:0191-2917e-ISSN:1943-7692 DownloadCaptionSymptoms of Macrophomina phaseolina in melon (R. Cohen et al.). Photo credit: R. Cohen. Jute plant infected with papaya ring spot virus (PRSV) (sample MG16-004) (C. Biswas et al.). Photo credit: V. Ramesh Babu. Metrics Downloaded 906 times Article History Issue Date: 7 Feb 2022Published: 8 Jan 2022First Look: 16 Jul 2021Accepted: 14 Jul 2021 Page: 321 Information© 2022 The American Phytopathological SocietyFundingNational Key Research Development Program of ChinaGrant/Award Number: 2017YFD0200308Grant/Award Number: [2018]5262National Natural Science Foundation of ChinaGrant/Award Number: 21977023Grant/Award Number: 31860515Program of Introducing Talents to Chinese UniversitiesGrant/Award Number: 111 ProgramGrant/Award Number: D20023KeywordsCamellia sinensisEpicoccum nigrummorphological characterizationmultilocus sequences analysespathogenicityThe author(s) declare no conflict of interest.PDF downloadCited ByFunctional Annotation, Prediction of Cis Target Gene for the Sequence of mRNAs, and Candidate Long Noncoding RNAs from Tea (Camellia sinensis var. sinensis) Leaves During Infection by the Fungal Pathogen Epicoccum nigrumHongke Huang, Yuanyou Yang, Chen Huang, Zhongqiu Xia, Yuqin Yang, Xinyue Jiang, Delu Wang, and Zhuo Chen26 July 2022 | PhytoFrontiers™, Vol. 0, No. 0Analysis of Competing Endogenous RNAs and MicroRNAs in Tea (Camellia sinensis) Leaves During Infection by the Leaf Spot Pathogen Pestalotiopsis trachicarpicolaYuqin Yang, Qiaoxiu Yin, Changlong Qiu, Zhongqiu Xia, Hongke Huang, Chen Huang, Xinyue Jiang, Yuanyou Yang, Delu Wang, and Zhuo Chen23 March 2022 | Molecular Plant-Microbe Interactions, Vol. 35, No. 5Full Issue PDF13 May 2022 | Molecular Plant-Microbe Interactions, Vol. 35, No. 5

Automatic Detection of Tomato Diseases Using Deep Transfer Learning

Abstract: Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control.

Diversity and biological activity of fungal endophytes of Zingiber officinale Rosc. with emphasis on Aspergillus terreus as a biocontrol agent of its leaf spot

Abstract: Leaf spot of ginger caused by Colletotrichum gloeosporioides is considered as a phytopathological constraint in the cultivation of ginger that has led to the economic loss of this valuable spice. Fungal endophytes serve as potent biocontrol agents for disease management because of their ability to colonize the same ecological niche similar to that of phytopathogen. The present study embarks on the isolation and identification of endophytic fungi from leaves and rhizomes of Zingiber officinale Rosc. A total of 563 endophytic fungal isolates were isolated which were grouped into 12 species belonging to 8 genera based on morphological characters and Internal Transcribed Spacers (ITS) sequence analysis. Among all the isolates, Aspergillus terreus possessed significant bioactivity profile and was subjected to bioassay guided fractionation. Various chromatographic and spectral analysis techniques led to the isolation and identification of an active molecule terrein. The purified compound displayed noteworthy antibacterial, antifungal and cytotoxic activities. Considering the antibacterial, cytotoxic and biocontrol potential of the fungus it could serve as a promising candidate in the field of agriculture and pharmaceutical industry. This is the first report on the biocontrol potential of fungal endophyte Aspergillus terreus and its bioactive metabolite terrein, against ginger leaf spot phytopathogen, C. gloeosporioides. Therefore, benefit of embracing such beneficial microorganism would be helpful in minimizing the use of agrochemicals and making agriculture more productive and sustainable. Further, terrein isolated from the A. terreus deserves special attention for its ability to serve as antimicrobial and anticancer agent.

Modeling Convolutional Neural Network for Detection of Plant Leaf Spot Diseases

Abstract: Farming has come a long way further than directly a system to provide food to an ever-increasing population. The seventieth population of Asian countries depends on agriculture. The identification of plant leaf diseases is typically carried out with the inspection by masters and organic tests. Both techniques are manual, costly and time-consuming. Detecting plant leaf diseases is a challenging task since it involves numerous variables such as genotype, environment, and their interplay. Accurate detection of plant leaf diseases involves a fundamental understanding of functional relativity and the collaborative factors. Deep learning helps in recognition of plant diseases and it provides earlier and timely detection of plant diseases for efficient disease management. It expresses a different infusion that gives systematic and automated detection of diseases. A convolutional neural network model is designed to use for detection of leaf spot diseases in plants. A tomato plant disease dataset has been generated from the local farms of Chittoor District, Andhra Pradesh, India. The tomato leaf spot diseases considered for training are Septoria leaf spot, bacterial spot, target spot and leaf mold which are most popular diseases in tomato plants. The average classification accuracy of the proposed model is determined to be 90.6%. For comparative analysis, the proposed CNN model has been experimentally evaluated with Multilayer perceptron classifier model. The proposed model produced an effective average MSE and R ² values of 0.016 and 0.991. The MLP model produced less optimal results than the proposed model thus confirming the efficacy of the proposed model in classifying the plant leaf diseases.

Trichoderma asperellum (NST-009): A potential native antagonistic fungus to control Cercospora leaf spot and promote the growth of ‘Green Oak’ lettuce (Lactuca sativa L.) cultivated in the commercial NFT hydroponic system

Abstract: Leaf spot caused by Cercospora lactucae-sativae is one of the most damaging diseases of ‘Green Oak’ lettuce in Thailand. This study was conducted to estimate the effectiveness of Trichoderma asperellum NST-009, a native strain in Thailand, to manage the leaf spot disease and enhance the growth of ‘Green Oak’ lettuce in a nutrient film technique (NFT) hydroponic system. In vitro tests showed that T. asperellum NST-009 significantly inhibited the mycelial growth of C. lactucae-sativae by 72.50%, and its antifungal metabolite from the culture filtrate of T. asperellum NST-009 inhibited the mycelial growth of C. lactucae-sativae by 93.26%. In the hydroponics experiment, T. asperellum NST-009 reduced the disease severity index by 67.51% compared to the inoculated control and significantly stimulated the growth of the ‘Green Oak’ lettuce in terms of the plant height (8.62%), canopy width (16.67%), leaf number (18.39%), shoot fresh weight (25.71%), root fresh weight (39.26%), and total P in the leaves (31.45%) compared to the control. In addition, T. asperellum NST-009 was found to survive in both the lettuce leaves and roots at 100.00%.

Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework

Abstract: Tomato is a widely consumed fruit across the world due to its high nutritional values. Leaf diseases in tomato are very common which incurs huge damages but early detection of leaf diseases can help in avoiding that. The existing practices for detecting different diseases by the human experts are costly, time consuming and subjective in nature. Computer vision plays important role toward early detection of tomato leaf detection. However, implementation of computationally less expensive model and improvement of detection performance is still open. This article reports a computer vision based system to classify seven different categories of diseases, namely, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, and target spots using optimized MobileNetV2 architecture. A modified gray wolf optimization approach has been adopted for optimization of MobileNetV2 hyperparameters for improved performance. The model has been validated using standard internal and external validation methods and found to provide the classification accuracy in the tune of 98%. The results reflect the promising potential of the presented framework for early detection of tomato leaf diseases which can help to avoid substantial agricultural loss.

Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

Abstract: Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

Identification of the Causal Agent of Brown Leaf Spot on Kiwifruit and Its Sensitivity to Different Active Ingredients of Biological Fungicides

Abstract: Kiwifruit (Actinidia chinensis) is an important commercial crop in China, and the occurrence of diseases may cause significant economic loss in its production. In the present study, a new pathogen that causes brown leaf spot disease on kiwifruit was reported. The fungus was isolated from an infected sample and identified as Fusarium graminearum based on morphological and molecular evaluation. Koch’s postulates were confirmed when the pathogen was re-isolated from plants with artificially induced symptoms and identified as F. graminearum. Based on the biological characteristics of the pathogen, it was determined that: its optimal growth temperature was 25 °C; optimal pH was 7; most suitable carbon source was soluble starch; most suitable nitrogen source was yeast powder; and best photoperiod was 12 h light/12 h dark. Further investigations were conducted by determining 50% effective concentrations (EC50) of several active ingredients of biological fungicides against F. graminearum. The results showed that among the studied fungicides, tetramycin and honokiol had the highest antifungal activity against this pathogen. Our findings provide a scientific basis for the prevention and treatment of brown leaf spot disease on kiwifruit.

First report of Colletotrichum black leaf spot on strawberry caused by Colletotrichum siamense in China

Abstract: In August 2020, a new fungal disease was observed on strawberry in Shandong Province, which showed that small spots of grayish-black, near round without lesions and haloes on the adaxial of strawberry leaves. The morphological features of colonies and conida on PDA medium were consistent with Colletotrichum gloeosporioides species complex. The rDNA internal transcribed spacer, calmodulin, chitin synthase, and actin from the isolates were amplified and sequenced. BLAST analysis of these four genes showed 99.24-100.00% identity with the corresponding sequences of C. siamense in GenBank. The result of phylogenetic analysis also indicated that the pathogen was identified as C. siamense. Similar symptoms were observed on the back of strawberry leaf after spraying conidial suspensions for 3 days, and C. siamense was reisolated which confirm the Koch's rule. This is the first report of new disease of black leaf spot on strawberry caused by C. siamense.

Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5

Abstract: The Rice crop in Agriculture field is playing an important role in economy of Pakistan and fulfilling the needs of living hood of human beings. The rice leaf faces several diseases like Bacterial Bligh, Brown Spot, Blast and Tungro. This research attempts to create a simple and best model for Rice leaf disease detection using deep learning model Yolov5. The model has been upgraded to v5 which is the latest version of Yolo. The performance and accuracy of object detection using Yolov5 is better than Yolov3 and Yolov4 models. This model is able to differentiate and successfully detect the rice leaf diseases. The Rice leaf images Dataset is downloaded from Kaggle website, the dataset contains 400 images of leaf infected by disease. This paper uses Google colab platform to train, validate and test the model for Rice Leaf disease detection. All necessary steps to be implemented, the rice leaf disease are detected and fully described. The developed model utilize epochs: 100. The experimental results show that the deep learning model created with 100 epochs has shown the best performance with precision, recall, and mAP value of 1.00, 0.94, and 0.62, respectively.

Impact of weather parameters on Alternaria leaf spot of soybean incited by Alternaria alternata

Abstract: Weather attributes play a crucial role in the infection process and spread of pathogen. Alternaria leaf spot incited by Alternaria alternata is most destructive disease of soybean appeared in southern and eastern parts of Rajasthan as well as India. The effect of various weather parameters along with different date of sowing on the development of Alternaria leaf spot in susceptible soybean cultivar RKS-24 was investigated during Kharif season 2018 and 2019. The various weather factors viz., temperature, relative humidity and rainfall under inoculated conditions and with staggered dates of sowing were taken to observe effect on disease progression and their effect on seed yield. The maximum increase in disease severity (57.82 and 58.22%) and AUDPC (389.45 and 394.42) recorded in crop sown on 18th June (inoculated on 8th July). Lowest disease severity (39.80 and 38.50%) and AUDPC (266.18 and 259.18) were observed during 39-43th standard meteorological week (September, 24-October, 28) in year 2018 and 2019, respectively. Maximum seed yield (1699 kg ha-1) was recorded in plants sown on 9th July, while, lowest seed yield was recorded in plants sown on 18th June with 1441.20 kg ha-1. The trend of disease severity and AUDPC value decreased from early sowing to late sowing (18th June-9th July). Major reasons were fluctuations in temperature, rainfall and relative humidity. It was also observed that the soybean plants for Alternaria leaf spot disease in early sowing were predisposed and so farmers should be advised to practice delayed sowing of soybean crop.

First Report of <i>Alternaria alternata</i> Causing Leaf Spot on Kidney Bean in China

Abstract: HomePlant DiseaseVol. 106, No. 5First Report of Alternaria alternata Causing Leaf Spot on Kidney Bean in China PreviousNext DISEASE NOTE OPENOpen Access licenseFirst Report of Alternaria alternata Causing Leaf Spot on Kidney Bean in ChinaYaZhong Jin, YaNan Xiong, ChangJian Xu, JinLi Ren, YongXia Guo, YuHu Zuo, YouLi Zhang, and XueQing GengYaZhong Jinhttps://orcid.org/0000-0003-0044-3111College of Horticulture and Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, ChinaNational Coarse Cereals Engineering Research Center, Daqing Heilongjiang, ChinaSearch for more papers by this author, YaNan XiongCollege of Horticulture and Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, ChinaSearch for more papers by this author, ChangJian XuCollege of Agronomy, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang, ChinaSearch for more papers by this author, JinLi RenCollege of Horticulture and Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, ChinaSearch for more papers by this author, YongXia Guo†Corresponding authors: Y. Gu; E-mail Address: [email protected], Y. Zuo; E-mail Address: [email protected], Y. Zhang; E-mail Address: [email protected], and X. Geng; E-mail Address: [email protected]National Coarse Cereals Engineering Research Center, Daqing Heilongjiang, ChinaCollege of Agronomy, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang, ChinaHeilongjiang Provincial Key Laboratory of Crop Pest Interaction Biology and Ecological Control, Daqing Heilongjiang, ChinaSearch for more papers by this author, YuHu Zuo†Corresponding authors: Y. Gu; E-mail Address: [email protected], Y. Zuo; E-mail Address: [email protected], Y. Zhang; E-mail Address: [email protected], and X. Geng; E-mail Address: [email protected]National Coarse Cereals Engineering Research Center, Daqing Heilongjiang, ChinaCollege of Agronomy, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang, ChinaHeilongjiang Provincial Key Laboratory of Crop Pest Interaction Biology and Ecological Control, Daqing Heilongjiang, ChinaSearch for more papers by this author, YouLi Zhang†Corresponding authors: Y. Gu; E-mail Address: [email protected], Y. Zuo; E-mail Address: [email protected], Y. Zhang; E-mail Address: [email protected], and X. Geng; E-mail Address: [email protected]National Coarse Cereals Engineering Research Center, Daqing Heilongjiang, ChinaCollege of Agronomy, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang, ChinaSearch for more papers by this author, and XueQing Geng†Corresponding authors: Y. Gu; E-mail Address: [email protected], Y. Zuo; E-mail Address: [email protected], Y. Zhang; E-mail Address: [email protected], and X. Geng; E-mail Address: [email protected]School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, ChinaSearch for more papers by this authorAffiliationsAuthors and Affiliations YaZhong Jin1 2 YaNan Xiong1 ChangJian Xu3 JinLi Ren1 YongXia Guo2 3 4 † YuHu Zuo2 3 4 † YouLi Zhang2 3 † XueQing Geng5 † 1College of Horticulture and Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, China 2National Coarse Cereals Engineering Research Center, Daqing Heilongjiang, China 3College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang, China 4Heilongjiang Provincial Key Laboratory of Crop Pest Interaction Biology and Ecological Control, Daqing Heilongjiang, China 5School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China Published Online:8 Apr 2022https://doi.org/10.1094/PDIS-09-21-2000-PDNAboutSectionsView articlePDFSupplemental ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat View articleKidney bean (Phaseolus vulgaris L.), also known as common bean, dry bean, or French bean, is one of the most valuable and highly nutritious legume crops cultivated and consumed worldwide (Blair et al. 2012; Choudhary et al. 2018). It is an important edible food and one of the most economically important vegetable crops in China. It is widely grown in Heilongjiang Province in China. In July 2020, leaf spot symptoms were found on old and new leaves of kidney bean plants in experimental fields in Zhaozhou County (N45°42′ 20.16″, E125°15′ 58.63″), Daqing City, Heilongjiang Province, China. The field had a disease incidence of approximately 20%. The leaf spot is conducive to onset at high temperatures and humidity, and spreads very quickly after rainy days, so it potentially is a large risk to the development of the kidney bean industry. In its early occurrence phase, infected leaves showed a yellowish halo in which the middle mesophyll lost its green color. Then, the yellow halo turned brown, and the middle leaf tissue of the halo appeared brown; ultimately the whole leaf had many brown spots. To isolate the pathogen, diseased tissue (5 × 5 mm) was excised from the margins of individual lesions from the leaves of diseased plants with typical symptoms, and was disinfected with 75% ethanol for 10 s followed by 2% NaClO for 3 min and then washed five to eight times with sterile water. Then, the samples were transferred to potato dextrose agar (PDA) plates and incubated. After 5 to 7 days of incubation at 25°C (Wei et al. 2018), the mycelia were dark green with white margins in obverse and dark in reverse. Conidiophores were light brown with 2 to 4 septa and obclavate, 17.5 to 44.0 × 6.5 to 14.5 μm, with a short beak, and with 1 to 5 transverse septa and 0 to 2 longitudinal septa, light brown to olive-brown. Based on morphological features and the sporulation pattern, the pathogen had characteristics similar to those described for Alternaria alternata (Fr.) Keissl. (Zhou et al. 2014), being identified as A. alternata. To confirm pathogenicity, the isolates were cultured on PCA for 7 days to prepare conidial suspensions with a final concentration of 1 × 108 spores/ml. Five potted kidney bean plants were sprayed with conidial suspensions, and five control potted plants were sprayed with sterile distilled water, in which these potted kidney bean plants were treated after wiping each leaf surface with 75% ethanol and washing each leaf with sterilized distilled water five times. These plants were incubated in an artificial growth chamber at 26 to 28°C with a 12 h light/dark photoperiod, with 85% relative humidity. After 3 days, yellowish halo lesions appeared on the inoculated plants, and pale lesions with distinct dark brownish red borders on kidney bean leaves were observed after 8 days, but no lesions were observed on the control leaves. Pathogenicity tests were repeated three times. The internal transcribed spacer (ITS) region of rDNA was amplified and sequenced with primers ITS1/ITS4. BLAST analysis of the sequences showed 100% sequence identity with a pathogenic A. alternata, and the nucleotide sequence of the ITS region was submitted to GenBank under accession MZ951052. In China, there are no detailed records about the causal agent of this disease on kidney bean in a paper in Chinese. To our knowledge, this is the first confirmed report of leaf spot caused by A. alternata on kidney bean in China.The author(s) declare no conflict of interest.References:Blair, M. W., et al. 2012. PLoS One 7:0049488. Crossref, ISI, Google ScholarChoudhary, N., et al. 2018. PLoS One 13:0191700. Crossref, ISI, Google ScholarWei, M., et al. 2018. Plant Dis. 102:2034. Google ScholarZhou, Z., et al. 2014. Plant Dis. 98:1588. https://doi.org/10.1094/PDIS-07-14-0726-PDN Link, ISI, Google ScholarFunding: This work was supported by Research and Development Plan of Applied Technology in Heilongjiang Province (GA19B104), and National Key R&D Program of China (2020YFD1001402).The author(s) declare no conflict of interest.DetailsFiguresLiterature CitedRelated Vol. 106, No. 5 May 2022SubscribeISSN:0191-2917e-ISSN:1943-7692 Download Metrics Article History Issue Date: 28 Apr 2022Published: 8 Apr 2022First Look: 11 Nov 2021Accepted: 4 Nov 2021 Page: 1531 Information© 2022 The American Phytopathological SocietyFundingResearch and Development Plan of Applied Technology in Heilongjiang ProvinceGrant/Award Number: GA19B104National Key R&D Program of ChinaGrant/Award Number: 2020YFD1001402KeywordsAlternaria alternatafungikidney beanThe author(s) declare no conflict of interest.PDF download

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

Abstract: Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.