Infected Leaf Research Articles

Detection and Differentiation of Xanthomonas translucens Pathovars translucens and undulosa from Wheat and Barley by Duplex Quantitative PCR.

Two probe-based quantitative PCR (qPCR) systems, namely P-Xtt and P-Xtu, were developed to diagnose cereal bacterial leaf streak pathogens Xanthomonas translucens pv. translucens and pv. undulosa, respectively. P-Xtt is specific to pv. translucens, and P-Xtu is specific to pv. undulosa, pv. cerealis, pv. secalis, and pv. pistaciae. P-Xtt and P-Xtu worked on all accessible strains of pv. translucens and pv. undulosa, respectively. Both systems could detect 100 copies of the target gBlock DNA. The two systems could be used in both singleplex qPCR and duplex qPCR with similar efficiencies. On genomic DNA from strains of various X. translucens pathovars, both singleplex and duplex qPCR could specifically detect and differentiate pv. translucens and pv. undulosa. The duplex qPCR could detect pv. translucens and pv. undulosa from genomic DNA of 1,000 bacterial cells. On infected barley and wheat grain samples and on one infected wheat leaf sample, the duplex qPCR showed similar efficiency compared to a previously published qPCR system but with the additional capability of pathovar differentiation. The duplex qPCR system developed in this study will be useful in studies on bacterial leaf streak and detection/differentiation of the pathogens.

Effectiveness of Volatiles Emitted by Streptomyces abikoensis TJGA-19 for Managing Litchi Downy Blight Disease.

Litchi is a fruit of significant commercial value; however, its quality and yield are hindered by downy blight disease caused by Peronophythora litchii. In this study, volatile organic compounds (VOCs) from Streptomyces abikoensis TJGA-19 were investigated for their antifungal effects and studied in vitro and in planta for the suppression of litchi downy blight disease in litchi leaves and fruits. The growth of P. litchii was inhibited by VOCs produced by TJGA-19 cultivated on autoclaved wheat seeds for durations of 10, 20, or 30 days. Volatiles from 20-day-old cultures were more active in inhibition effect against P. litchii than those from 10- or 30-day-old cultures. These volatiles inhibit the growth of mycelia, sporulation, and oospore production, without any significant effect on sporangia germination. Additionally, the VOCs were effective in suppressing disease severity in detached litchi leaf and fruit infection assays. With the increase in the weight of the wheat seed culture of S.abikoensis TJGA-19, the diameters of disease spots on leaves, as well as the incidence rate and disease indices on fruits, decreased significantly. Microscopic results from SEM and TEM investigations showed abnormal morphology of sporangia, mycelia, and sporangiophores, as well as organelle damage in P. litchii caused by VOCs of TJGA-19. Spectroscopic analysis revealed the identification of 22 VOCs produced by TJGA-19, among which the most dominant compound was 2-Methyliborneol. These findings indicated the significant role of TJGA-19 compounds in the control of litchi downy blight disease and in improving fruit quality.

Long amplicon nanopore sequencing of Botrytis cinerea and other fungal species present in infected grapevine leaf samples.

Botrytis cinerea is a well-known plant pathogen responsible for grey mould disease infecting more than 500 plant species. It is listed as the second most important plant pathogen scientifically and economically. Its impact is particularly severe in grapes since it affects both the yield of grape berries and the quality of wines. While various methods for detecting B. cinerea have been investigated, the application of Oxford Nanopore Technology (ONT) for complete ribosomal operon sequencing, which has proven effective in human and animal fungal research and diagnostics, has not yet been explored in grapevine (Vitis vinifera) disease research. In this study, we sequenced complete ribosomal operons (∼5.5 kb amplicons), which encompass the 18S, ITS1, 5.8S, ITS2, and 28S regions, from both pure cultures of B. cinerea and infected grapevine leaf samples. Minimap2, a sequence alignment tool integrated into the EPI2ME software, served as a taxonomy classifier, utilizing the custom reference database FRODO. The results demonstrate that B. cinerea was detectable when this pathogen was not the dominant fungal species in leaf samples. Additionally, the method facilitates host DNA-free sequencing and might have a good potential to distinguish other pathogenic and non-pathogenic fungal species hosted within grapevine's infected leaves, such as Alternaria alternata, Saccharomyces cerevisiae, Saccharomyces boulardii, Mucor racemosus, and Ascochyta rabie. The sequences were uploaded to the NCBI database. Long amplicon sequencing method has the capacity to be broadened to other susceptible crops and pathogens, as a valuable tool for early grey rot detection and mycobiome research. Future large-scale studies are needed to overcome challenges, such as comprehensive reference databases for complete fungal ribosomal operons for grape mycobiome studies.

Double-Stranded RNA Targeting White Mold Sclerotinia sclerotiorum Argonaute 2 for Disease Control via Spray-Induced Gene Silencing.

Sclerotinia sclerotiorum, the causal agent of white mold infection, is a cosmopolitan fungal pathogen that causes major yield losses in many economically important crops. Spray-induced gene silencing has recently been shown to be a promising alternative method for controlling plant diseases. Based on our prior research, we focused on developing a spray-induced gene silencing approach to control white mold by silencing S. sclerotiorum argonaute 2 (SsAgo2), a crucial part of the fungal small RNA pathway. We compared the lesion size as a result of targeting each ∼500-bp segment of SsAgo2 from the 5' to the 3' end and found that targeting the PIWI/RNaseH domain of SsAgo2 is most effective. External application of double-stranded RNA (dsRNA)-suppressed white mold infection using either in vitro or in vivo transcripts was determined at the rate of 800 ng/0.2 cm2 area with a downregulation of SsAgo2 from infected leaf tissue confirmed by RT-qPCR. Furthermore, magnesium/iron-layered double hydroxide nanosheets loaded with in vitro- and in vivo-transcribed dsRNA segments significantly reduced the rate of S. sclerotiorum lesion expansion. In vivo-produced dsRNA targeting the PIWI/RNaseH domain of the SsAgo2 transcript showed increased efficacy in reducing the white mold symptoms of S. sclerotiorum when combined with layered double hydroxide nanosheets. This approach is promising to produce a large scale of dsRNA that can be deployed as an environmentally friendly fungicide to manage white mold infections in the field.

Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves.

Oak powdery mildew, caused by the biotrophic fungus Erysiphe alphitoides, is a prevalent disease affecting oak trees, such as English oak (Quercus robur). While mature oak populations are generally less susceptible to this disease, it can endanger young oak seedlings and new leaves on mature trees. Although disruptions of photosynthate and carbohydrate translocation have been observed, accurately detecting and understanding the specific biomolecular interactions between the fungus and the leaves of oak trees is currently lacking. Herein, via hybrid Raman spectroscopy combined with an advanced artificial neural network algorithm, the underpinning biomolecular interactions between biological soft matter, i.e., Quercus robur leaves and Erysiphe alphitoides, are investigated and profiled, generating a spectral library and shedding light on the changes induced by fungal infection and the tree's defence response. The adaxial surfaces of oak leaves are categorised based on either the presence or absence of Erysiphe alphitoides mildew and further distinguishing between covered or not covered infected leaf tissues, yielding three disease classes including healthy controls, non-mildew covered and mildew-covered. By analysing spectral changes between each disease category per tissue type, we identified important biomolecular interactions including disruption of chlorophyll in the non-vein and venule tissues, pathogen-induced degradation of cellulose and pectin and tree-initiated lignification of cell walls in response, amongst others, in lateral vein and mid-vein tissues. Via our developed computational algorithm, the underlying biomolecular differences between classes were identified and allowed accurate and rapid classification of disease with high accuracy of 69.6% for non-vein, 73.5% for venule, 82.1% for lateral vein and 85.6% for mid-vein tissues. Interfacial wetting differences between non-mildew covered and mildew-covered tissue were further analysed on the surfaces of non-vein and venule tissue. The overall results demonstrated the ability of Raman spectroscopy, combined with advanced AI, to act as a powerful and specific tool to probe foliar interactions between forest pathogens and host trees with the simultaneous potential to probe and catalogue molecular interactions between biological soft matter, paving the way for exploring similar relations in broader forest tree-pathogen systems.

Deep Convolution Neural Network to solve Problems for Appel Leaf Disease Detection

Orchardists are now concerned about apple leaf infections since they could result in crop failure. It is challenging for growers to identify the type of illness on apple leaves due to the large variety of diseases that can affect apple leaves. In this study, we cover four different illness categories: healthy, numerous diseased, rusty, and scabby. a deep convolutional neural network method of processing. using a number of suggested methods, including data preprocessing and the pre-configured VGG-16 deep convolutional neural network (CNN) architecture. The Adam optimization model's beta 2 = 0.999 parameter value at Ephoch to 85/100 with an accuracy of 0.7582 and epsilon = 1e-07 parameter value at Ephoch to 32/100 with an accuracy of 0.7582 both produced the best accuracy outcomes.

Comparative analysis of paddy leaf diseases sensing with a hybrid convolutional neural network model

The major food in south India is Paddy. Early detection of leaf disease can increase the production of crops. In the agricultural field, farmers can control the spreading of leaf disease with the help of the modeling of automated disease classification. In comparison to the manual approach to paddy leaf infection detection via visual examination, automated identification of rice illnesses using an image of the paddy leaf may be advantageous. Deep learning, a highly well-liked and effective machine learning method, has recently demonstrated considerable promise in the categorization task of images. In this work, Contrast Limited Adaptive Histogram Equalization (CLAHE) pre-processes the augmented images of paddy leaves. The Gray level co-occurrence metrics (GLCM) model then extracts the features from an image of the pre-processed paddy leaves. The five disease classes of paddy leaves were finally identified using the hybrid CNN model as follows: Bacterial Leaf Blight (BLB), Sheath Rot (SR), Narrow Brown Leaf Spot (NBLS), Brown Spot (BS) and Leaf Smut (LS). MATLAB platforms handle implementation works. While compared to the previous approaches, the proposed model reveals the superiority of paddy leaves classification.

Disease Detection using Region-Based Convolutional Neural Network and ResNet

In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including Convolutional Neural Networks(CNN), Resnet, ReLu enabling the early detection of infections. The deep learning models leverage the captured images to identify and classify infections at their initial stages. The usage of R-CNN and ResNet technology in agriculture field has brought the tremendous change when we detect the disease in earlier stage of crop. Thus the farmer can take the pest preventive measures in the beginning stage to avoid crop failure.

Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model

ABSTRACT Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also, the disease’s adhesion and dimension can overlap, causing partial under-segmentation. Therefore, a novel robust Deep Encoder-Decoder Cascaded Network (DEDCNet) model is proposed in this manuscript for leaf image segmentation that precisely segments the diseased leaf spots and differentiates similar diseases. This model is comprised of an Infected Spot Recognition Network and an Infected Spot Segmentation Network. Initially, ISRN is designed by integrating cascaded CNN with a Feature Pyramid Pooling layer to identify the infected leaf spot and avoid an impact of background details. After that, the ISSN developed using an encoder-decoder network, which uses a multi-scale dilated convolution kernel to precisely segment the infected leaf spot. Moreover, the resultant leaf segments are provided to the pre-learned CNN models to learn texture features followed by the SVM algorithm to categorize leaf disease classes. The ODEDCNet delivers exceptional performance on both the Betel Leaf Image and PlantVillage datasets. On the Betel Leaf Image dataset, it achieves an accuracy of 94.89%, with high precision (94.35%), recall (94.77%), and F-score (94.56%), while maintaining low under-segmentation (6.2%) and over-segmentation rates (2.8%). It also achieves a remarkable Dice coefficient of 0.9822, all in just 0.10 seconds. On the PlantVillage dataset, the ODEDCNet outperforms other existing models with an accuracy of 96.5%, demonstrating high precision (96.61%), recall (96.5%), and F-score (96.56%). It excels in reducing under-segmentation to just 3.12% and over-segmentation to 2.56%. Furthermore, it achieves a Dice coefficient of 0.9834 in a mere 0.09 seconds. It evident for the greater efficiency on both segmentation and categorization of leaf diseases contrasted with the existing models.

USMA-BOF: A Novel Bag-of-Features Algorithm for Classification of Infected Plant Leaf Images in Precision Agriculture

USMA-BOF: A Novel Bag-of-Features Algorithm for Classification of Infected Plant Leaf Images in Precision Agriculture

Histopathological Investigation of Varietal Responses to Cercospora beticola Infection Process on Sugar Beet Leaves.

Cercospora leaf spot (CLS) is the most destructive foliar disease in sugar beet (Beta vulgaris). It is caused by Cercospora beticola Sacc., a fungal pathogen that produces toxins and enzymes which affect membrane permeability and cause cell death during infection. In spite of its importance, little is known about the initial stages of leaf infection by C. beticola. Therefore, we investigated the progression of C. beticola on leaf tissues of susceptible and resistant sugar beet varieties at 12-h intervals during the first 5 days after inoculation using confocal microscopy. Inoculated leaf samples were collected and stored in DAB (3,3'-diaminobenzidine) solution until processed. Samples were stained with Alexa Fluor-488-WGA dye to visualize fungal structures. Fungal biomass accumulation, reactive oxygen species (ROS) production, and the area under the disease progress curve were evaluated and compared. ROS production was not detected on any variety before 36 h postinoculation (hpi). C. beticola biomass accumulation, percentage leaf cell death, and disease severity were all significantly greater in the susceptible variety compared with the resistant variety (P < 0.05). Conidia penetrated directly through stomata between 48 to 60 hpi and produced appressoria on stomatal guard cells at 60 to 72 hpi in susceptible and resistant varieties, respectively. Penetration of hyphae inside the parenchymatous tissues varied in accordance with time postinoculation and varietal genotypes. Overall, this study provides a detailed account to date of events leading to CLS disease development in two contrasting varieties.

Auto-metric graph neural network for paddy leaf disease classification

In traditional methods, rice plant diseases can be identified individually by experts, but laboratory testing takes a lot of time. As a result, it reduces agricultural productivity and causes economic losses for farmers. To address these, rapid and effective systems for identifying and classifying rice plant diseases need to be developed. This article proposes an Auto-metric Graph Neural Network (AGNN) for the classification of paddy leaf disease. Initially, images are gathered via the dataset of Rice Leaf Disease Image Samples. The collected images are preprocessing by utilizing Anisotropic Diffusion Filter-Based Unsharp Masking with crispening. These preprocessed paddy leaf images are supplied to Bayesian fuzzy clustering for the purpose of segmentation. The segmented pictures are fed to AGNN for categorizing paddy leaf infection image as Bacterial Blight, Blast, Brown Spot and Tungro. The paddy leaf disorder classification-AGNN technique attains accuracies of 8.65%, 10.76%, 18.34% and 13.19% compared to the existing methods.

Computational identification, characterization, and expression analysis of MLO genes in two powdery mildew resistant and susceptible lines of sunflower

Sunflower is one of the major oilseeds producing crops in the world. However, the crop production is affected by powdery mildew in the tropics, caused by a fungal pathogen Golovinomyces latisporus. A step towards developing genetic resistance is by identifying potential genes involved. The MLO genes when silenced provide durable and broad-spectrum resistance. In this study, we identified the MLO gene family in the genome of Sunflower using computational methods. We analyzed the chromosomal distribution, gene structure conserved motifs, and phylogenetic relationship of sunflower MLO genes with MLO genes of other species. A total of 38 MLO genes were identified which are distributed unevenly on the chromosomes with varied intron/exon gene structures. These were phylogenetically distributed into six different groups. The cis elements identified in the promoter region had hormone responsive, stress responsive, development related elements along with transcription factors. We developed recombinant inbred lines by crossing H. annuus and H. praecox (F7 generation). Further, we performed RNAseq analysis of transcriptomes sequenced from powdery mildew infected leaf samples of PMR-16 (resistant) and PMS-27 (susceptible) varieties of the recombinant inbred lines. Expression analysis revealed 12 differentially expressed genes between both varieties. Our results can be used as a reference for future functional studies.

De novo transcriptome assembly of Dalbergia sissoo Roxb. (Fabaceae) under Botryodiplodia theobromae-induced dieback disease

Dalbergia sissoo Roxb. (Shisham) is a timber-producing species of economic, cultural, and medicinal importance in the Indian subcontinent. In the past few decades, Shisham's dieback disease caused by the fungus Botryodiplodia theobromae has become an evolving issue in the subcontinent endangering its survival. To gain insights into this issue, a standard transcriptome assembly was deployed to assess the response of D. sissoo at the transcriptomic level under the stress of B. theobromae infection. For RNA isolation, the control and infected leaf tissue samples were taken from 1-year-old greenhouse-grown D. sissoo plants after 20 days of stem-base spore inoculation. cDNA synthesis was performed from these freshly isolated RNA samples that were then sent for sequencing. About 18.14 Gb (Giga base) of data was generated using the BGISEQ-500 sequencing platform. In terms of Unigenes, 513,821 were identified after a combined assembly of all samples and then filtering the abundance. The total length of Unigenes, their average length, N50, and GC-content were 310,523,693 bp, 604 bp, 1,101 bp, and 39.95% respectively. The Unigenes were annotated using 7 functional databases i.e., 200,355 (NR: 38.99%), 164,973 (NT: 32.11%), 123,733 (Swissprot: 24.08%), 142,580 (KOG: 27.75%), 139,588 (KEGG: 27.17%), 99,752 (GO: 19.41%), and 137,281 (InterPro: 26.72%). Furthermore, the Transdecoder detected 115,762 CDS. In terms of SSR (Simple Sequence Repeat) markers, 62,863 of them were distributed on 51,508 Unigenes and on the predicted 4673 TF (Transcription Factor) coding Unigenes. A total of 16,018 up- and 19,530 down-regulated Differentially Expressed Genes (DEGs) were also identified. Moreover, the Plant Resistance Genes (PRGs) had a count of 9230. We are hopeful that in the future, these identified Unigenes, SSR markers, DEGs and PRGs will provide the prerequisites for managing Shisham dieback disease, its breeding, and in tree improvement programs.

Screening of onion (Allium cepa) genotypes to find out novel resistant source against purple blotch (Alternaria porri)

Purple blotch caused by Alternaria porri (Ellis) Cif., 1930 causes huge losses to bulb crop and seed crop of onion (Allium cepa L.). This is a major disease affecting successful onion production in majority of the onion growing areas. To identify resistant source against purple blotch, 34 onion genotypes with good marketable yield based on field evaluation trial in 2017 and allied species Pusa Soumya were screened under protected conditions during kharif, 2018 at insect proof net house facility of ICAR-Indian Agricultural Research Institute, New Delhi. The pure culture of Alternaria porri, isolated from infected leaf samples of onion crop raised under natural epiphytotic conditions during kharif, 2017. Highest change of disease severity index was observed between 1–2 weeks post inoculation (WPI) while highest disease severity index was recorded at 5 WPI suggesting that preventive measures must be employed as soon as disease symptoms appear in the field. The disease severity index ranged from 0.10–33.33 at 1 WPI, which increased to 9.33–89.33 at 5 WPI. The cultivated allied species Pusa Soumya was identified as novel resistance source with DSI value less than 10.0% at 5 WPI. Four genotypes exhibited moderately susceptible reaction, while 20 genotypes were susceptible and 10 genotypes were highly susceptible. The area under disease progress curve (AUDPC) value ranged from 22.33–240.17. Pusa Soumya had the lowest value and among onion lines Arka Kirtiman was found promising. Three onion genotypes had low relative AUDPC value and can effectively be utilized in resistance breeding programme. The average daily temperature between 20–25°C and relative humidity above 75% was found ideal for disease development.

Long noncoding RNAs emerge from transposon-derived antisense sequences and may contribute to infection stage-specific transposon regulation in a fungal phytopathogen

BackgroundThe genome of the obligate biotrophic phytopathogenic barley powdery mildew fungus Blumeria hordei is inflated due to highly abundant and possibly active transposable elements (TEs). In the absence of the otherwise common repeat-induced point mutation transposon defense mechanism, noncoding RNAs could be key for regulating the activity of TEs and coding genes during the pathogenic life cycle.ResultsWe performed time-course whole-transcriptome shotgun sequencing (RNA-seq) of total RNA derived from infected barley leaf epidermis at various stages of fungal pathogenesis and observed significant transcript accumulation and time point-dependent regulation of TEs in B. hordei. Using a manually curated consensus database of 344 TEs, we discovered phased small RNAs mapping to 104 consensus transposons, suggesting that RNA interference contributes significantly to their regulation. Further, we identified 5,127 long noncoding RNAs (lncRNAs) genome-wide in B. hordei, of which 823 originated from the antisense strand of a TE. Co-expression network analysis of lncRNAs, TEs, and coding genes throughout the asexual life cycle of B. hordei points at extensive positive and negative co-regulation of lncRNAs, subsets of TEs and coding genes.ConclusionsOur work suggests that similar to mammals and plants, fungal lncRNAs support the dynamic modulation of transcript levels, including TEs, during pivotal stages of host infection. The lncRNAs may support transcriptional diversity and plasticity amid loss of coding genes in powdery mildew fungi and may give rise to novel regulatory elements and virulence peptides, thus representing key drivers of rapid evolutionary adaptation to promote pathogenicity and overcome host defense.

Plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm.

Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of continuing surveillance. In this manuscript, plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm (PDI-CMAE-DDAOA) is proposed. The plant village dataset is used to collect the images. Then the image is fed to preprocessing. Using an adaptive self-guided filter approach, the noise is removed from the input images during the pre-processing phase. The result of the pre-processing section serves as input for the feature extraction segment. Four statistical features, including mean, variance, entropy, and kurtosis, are recovered from the cosine similarity hidden Markov model (CSHMM). The contextual mask auto-encoder (CMAE) is given the extracted features to accurately classify the healthy and unhealthy regions of the plant image. The issue of slow convergence affects the CMAE. However, it is noted that the CMAE converges more quickly with deep learning features than with texture features in this instance. The CMAE classifier generally does not exhibit any adaptation of optimization algorithms for determining the best parameters to ensure the precise classification of plant disease. Therefore, dynamic differential annealed optimization algorithm (DDAOA) is considered to enhance the CMAE classifier, which accurately distinguishes between healthy and diseased plants. The proposed PDI-CMAE-DDAOA is done in Python. The efficacy of PDI-CMAE-DDAOA is evaluated under some performance metrics, like accuracy, precision, sensitivity, F1-score, specificity, error rate, receiver operating characteristic curve (ROC), computational time. The proposed method provides higher accuracy 23.34%, 34.33%, and 32.07%; higher sensitivity 36.67%, 36.33%, and 23.21%; higher F1-score 46.67%, 57.56%, and 43.21%; higher specificity 56.67%, 67.56%, and 23.21% analyzed with existing models, like transfer learning-based deep ensemble neural network for plant leaf infection recognition (PDI-DENN), plant disease detection with hybrid model based on convolutional auto-encoder and convolutional neural network (PDI-CAE-CNN), and automatic and reliable leaf disease finding depending on deep learning methods (PDI-EN-CNN), respectively. RESEARCH HIGHLIGHTS: To find the plant disease at early stage. To present PDI-CMAE-DDAOA. To get better classification accuracy by extracting the optimal features with the help of efficient CSHMM. To minimize the error during classification process. To maximize high area under curve value.

Establishment of Rice Plant Leaf Infection through Fungal Inoculation via Wounding

Establishment of Rice Plant Leaf Infection through Fungal Inoculation via Wounding

Establishment of Rice Plant Leaf Infection through Fungal Inoculation via Wounding

Establishment of Rice Plant Leaf Infection through Fungal Inoculation via Wounding

Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.