Blight Research Articles

Precision mapping and expression analysis of recessive bacterial blight resistance gene xa-45(t) from Oryza glaberrima.

Bacterial blight, caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most devastating diseases of rice leading to huge yield losses in Southeast Asia. The recessive resistance gene xa-45(t) from Oryza glaberrima IRGC102600B, mapped on rice chromosome 8, spans 80Kb with 9 candidate genes on Nipponbare reference genome IRGSP-1.0. The xa-45(t) gene provides durable resistance against all the ten Xanthomonas pathotypes of Northern India, thus aiding in the expansion of recessive bacterial blight resistance gene pool. Punjab Rice PR127, carrying xa-45(t), was released for wider use in breeding programs. This study aims to precisely locate the target gene among the 9 candidates conferring resistance to bacterial blight disease. Sanger sequencing of all nine candidate genes revealed seven SNPs and an Indel between the susceptible parent Pusa 44 and the resistant introgression line IL274. The genotyping with polymorphic markers identified three recombinant breakpoints for LOC_Os08g42370, and LOC_Os08g42400, 15 recombinants for LOC_Os08g423420 and 26 for LOC_Os08g42440 out of 190 individuals. Relative expression analysis across six time intervals (0, 8, 24, 48, 72, and 96h) after bacterial blight infection showed over expression of LOC_Os08g42410-specific transcripts in IL274 compared to Pusa 44, with a significant 4.46-fold increase observed at 72h post-inoculation. The Indel marker at the locus LOC_Os08g42410 was found co-segregating with the phenotype, suggesting its candidacy towards xa-45(t). The transcript abundance assay provides strong evidence for the involvement of LOC_Os08g42410 in the resistance conferred by the bacterial blight gene xa-45(t).

Herbicide safener isoxadifen-ethyl associated with increased Goss's wilt severity in corn (Zea mays).

Goss's bacterial wilt and leaf blight (Goss's wilt), caused by the bacterium Clavibacter nebraskensis, is a corn disease that has been a top ten yield-reducing disease in North America in the past 15 years. Isoxadifen-ethyl is an herbicide safener that effectively increases cytochrome P450 activity in corn which enhances a plant's metabolism of herbicide molecules. Recent research found a potential link between isoxadifen-ethyl and increased Goss's wilt severity. The application of isoxadifen-ethyl increased (P = 0.014-0.046) area under disease progress curve (AUDPC) by 19%, 7%, and 9% at three environments, independent of accompanying herbicide or herbicide application timing. However, no significant differences in incidence of systemic wilt or corn grain yield occurred among treatments at any environment. These data provide evidence for an association between isoxadifen-ethyl safener and Goss's wilt in corn. The reason for this association is unknown, but the safener may affect plant or pathogen physiological mechanisms. While the increased disease severity did not result in decreased grain yield in these experiments, an increase in pathogen inoculum due to higher disease severity could influence Goss' wilt epidemics in future years. © 2024 Society of Chemical Industry.

Bacterial blight: Once a menacing disease of cotton in India, now tamed and fading from research spotlight

AbstractBacterial blight, incited by Xanthomonas citri pv. malvacearum (Xcm), is historically one of the most devastating diseases of cotton globally. In India, the initial documentation of the disease dates back to 1918 and it assumed a great magnitude after 1947, primarily triggered by cultivation of tetraploid cotton, as the earlier cultivated indigenous diploid cotton was naturally resistant to the pathogen, and became widespread during the 1970s. Among 20 races of bacterial blight pathogen documented at global level, Race 18 is the most virulent one and prominent in India. The pathogen inflicts damage at all crop stages, beginning with seedlings. The disease caused regular yield losses of 30%–35% in India and had become a formidable adversary to cotton cultivation in the country; however, over the years its appearance diminished. Much larger gains were apparently achieved when disease was managed with a combination of seed treatment, foliar sprays and bio‐agents. Furthermore, extensive breeding programmes led to development of resistant cultivars, which proved instrumental in managing the disease. The disease exhibited a sharp decline from the beginning of the 21st century and is now no longer a chronic problem in India. This comprehensive review attempts to provide insight into the dynamic nature of bacterial blight threatening cotton cultivation in India during the last century and the underpinning multifaceted research efforts that paved the way for countering the disease. An attempt is made to discuss the qualitative and quantitative studies on genetics of bacterial blight for facilitating the breeding programmes.

High resistance risk of Phytophthora colocasiae to azoxystrobin in southeastern of China

Quinone outside inhibitor (QoI) has been applied to manage taro leaf blight caused by Phytophthora colocasiae in southeastern of China for many years. The risk of P. colocasiae to QoI and the potential resistant mechanism remain unknown. In this study, the 74 P. colocasiae strains were sampled from southeastern of China. Sequence analysis of the QoI target Cytb showed one nucleotide variant in the fragment of this gene in this population, producing two haplotypes. The nucleotide variant leads to codon change at 142 (GGT to GCT) producing A142 (alanine) and G142 (glycine) in Hap_1 and Hap_2 strains, respectively. The sensitivity differentiation to azoxystrobin of two haplotypes were observed in vitro. The Hap_1 and Hap_2 strains were confirmed resistant and sensitive by control efficacy of label rate fungicide application, which was 3.0% and 88.8% treated with 500 μg/mL azoxystrobin, respectively. In addition, 10.0 μg/mL azoxystrobin plus 50 μg/mL salicylhydroxamic acid (SHAM) supplemented in PDA medium was identified as a discriminatory dose for differentiation of these two phenotype strains. The azoxystrobin resistant frequency reached 86.5%, indicating prevalence of QoI resistance in the field. Further fitness related features showed that no significant difference in temperature sensitivity, mycelial growth rate, sporangia production, zoospore release and aggressiveness between azoxystrobin-resistant and sensitive strains indicating no potential fitness cost for azoxystrobin resistance. Taken together, azoxystrobin resistance need to be taken into consideration to manage taro leaf blight in southeastern of China.

Understanding the nature of blast resistance in combined bacterial leaf blight and blast gene pyramided lines of rice variety tellahamsa.

Rice blast and bacterial leaf blight (BLB) are the most limiting factors for rice production in the world which cause yield losses typically ranging from 20 to 30% and can be as high as 50% in some areas of Asia especially India under severe infection conditions. An improved line of Tellahamsa, TH-625-491 having two BLB resistance genes (xa13 and Xa21) and two blast resistance genes (Pi54 and Pi1) with 95% Tellahamsa genome was used in the present study. TH-625-491 was validated for all four target genes and was used for backcrossing with Tellahamsa. Seventeen IBC1F1 plants heterozygous for all four target genes, 19 IBC1F2 plants homozygous for four, three and two gene combinations and 19 IBC1F2:3 plants also homozygous for four, three and two gene combinations were observed. Among seventeen IBC1F1 plants, IBC1F1-62 plant recorded highest recurrent parent genome (97.5%) covering 75 polymorphic markers. Out of the total of 920 IBC1F2 plants screened, 19 homozygous plants were homozygous for four, three and two target genes along with bacterial blight resistance. Background analysis was done in all 19 homozygous IBC1F2 plants possessing BLB resistance (possessing xa13, Xa21, Pi54 and Pi1 in different combinations) with five parental polymorphic SSR markers. IBC1F2-62-515 recovered 98.5% recurrent parent genome. The four, three and two gene pyramided lines of Tellahamsa exhibited varying resistance to blast. Results show that there might be presence of antagonistic effect between bacterial blight and blast resistance genes since the lines with Pi54 and Pi1 combination are showing better resistance than the combinations with both bacterial blight and blast resistance genes.

Resource-efficient federated learning over IoAT for rice leaf disease classification

Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15 % of protein intake and 21 % of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99 %, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification.

XA21-mediated resistance to Xanthomonas oryzae pv. oryzae is dose dependent.

The rice receptor kinase XA21 confers broad-spectrum resistance to Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of rice bacterial blight disease. To investigate the relationship between the expression level of XA21 and resulting resistance, we generated independent HA-XA21 transgenic rice lines accumulating the XA21 immune receptor fused with an HA epitope tag. Whole-genome sequence analysis identified the T-DNA insertion sites in sixteen independent T0 events. Through quantification of the HA-XA21 protein and assessment of the resistance to Xoo strain PXO99 in six independent transgenic lines, we observed that XA21-mediated resistance is dose dependent. In contrast, based on the four agronomic traits quantified in these experiments, yield is unlikely to be affected by the expression level of HA-XA21. These findings extend our knowledge of XA21-mediated defense and contribute to the growing number of well-defined genomic landing pads in the rice genome that can be targeted for gene insertion without compromising yield.

CRISPR/Cas9-generated mutations in a sugar transporter gene reduce cassava susceptibility to bacterial blight.

Bacteria from the genus Xanthomonas are prolific phytopathogens that elicit disease in over 400 plant species. Xanthomonads carry a repertoire of specialized proteins called transcription activator-like (TAL) effectors that promote disease and pathogen virulence by inducing the expression of host susceptibility (S) genes. Xanthomonas phaseoli pv. manihotis (Xpm) causes bacterial blight on the staple food crop cassava (Manihot esculenta Crantz). The Xpm effector TAL20 induces ectopic expression of the S gene Manihot esculenta Sugars Will Eventually be Exported Transporter 10a (MeSWEET10a), which encodes a sugar transporter that contributes to cassava bacterial blight (CBB) susceptibility. We used CRISPR/Cas9 to generate multiple cassava lines with edits to the MeSWEET10a TAL20 effector binding site and/or coding sequence. In several of the regenerated lines, MeSWEET10a expression was no longer induced by Xpm, and in these cases, we observed reduced CBB disease symptoms post Xpm infection. Because MeSWEET10a is expressed in cassava flowers, we further characterized the reproductive capability of the MeSWEET10a promoter and coding sequence mutants. Lines were crossed to themselves and to wild-type plants. The results indicated that expression of MeSWEET10a in female, but not male, flowers is critical to produce viable F1 seed. In the case of promoter mutations that left the coding sequence intact, viable F1 progeny were recovered. Taken together, these results demonstrate that blocking MeSWEET10a induction is a viable strategy for decreasing cassava susceptibility to CBB and that ideal lines will contain promoter mutations that block TAL effector binding while leaving endogenous expression of MeSWEET10a unaltered.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

Characterization of Exserohilum isolates associated with northern corn leaf blight in Brazil

Characterization of Exserohilum isolates associated with northern corn leaf blight in Brazil

Efficiency of inoculation methods for the assessment of bacterial halo blight in coffee seedlings

AbstractBacterial halo blight (BHB), caused by Pseudomonas coronafaciens pv. garcae (Pcg), is one of the most significant diseases affecting coffee trees worldwide, resulting in considerable damage and notable losses within coffee production systems. Further research into the epidemiology and aetiology of BHB in coffee plants is essential for developing effective management strategies. Understanding the efficiency of each inoculation methodology in inducing symptoms is crucial for advancing this research. Therefore, this study aimed to assess the efficiency of seven Pcg inoculation methods using five concentrations to enhance the highest BHB intensity. The experimental design was a complete randomized block design in a factorial (7 × 5) variance analysis with four replications. The first three pairs of leaves of coffee seedlings of the cultivar Catuaí Vermelho IAC‐99 were inoculated and evaluated for the incidence and severity of BHB. The inoculation methods of Pcg by injury provided higher BHB AUDPCI and AUDPCS values. Among these, the inoculum injection and multi‐needle wounding at inoculum concentrations of 1.6 × 109 CFU mL−1 demonstrated higher disease intensity.

Identification of resistant donors for turcicum leaf blight disease of maize among cold tolerant temperate germplasm

Turcicum leaf blight disease of maize is the major biotic factor limiting maize production under temperate agro-climatic conditions of Kashmir. Deployment of disease resistant cultivars is the most effective, ecofriendly and cost-efficient disease management option under these conditions where chemical control of maize diseases is not adopted. In the present study, temperate maize germplasm was evaluated against Exherohilum turcicum causing turcicum leaf blight of maize. Among the evaluated maize genotypes some early maturing cold tolerant inbred lines registered high level of resistance (HR) with disease score 1 in 1 to 9 evaluation scale which can be used profitably in resistance breeding programme for the development of high yielding maize varieties resistant to turcicum leaf blight.

Antifungal Potential of Ocimum sanctum Linn. Essential Oil against Fungal Pathogens Causing Economic Losses in Cymbopogon flexuosus Nees Ex Steud. (Lemongrass)

Cymbopogon flexuosus Nees Ex Steud. (Commonly known as lemongrass, family Poaceae) is one of the commercially important aromatic plants having medicinal properties. India is one of the largest producers and exporters of lemongrass essential oil in the world; in the year 2021-22, India exported about 610 tons of lemongrass oil, contributing more than 4 lakh US $ million in Gross Domestic Productivity (GDP). However, despite a good national and international market, this crop suffers quantitative and qualitative losses due to the leaf spot and leaf blight diseases caused by the fungi Curvularia trifolii and Rhizoctonia solani, respectively; therefore, an attempt has been made to manage these fungal pathogens. The essential oil of basil (Ocimum sanctum Linn.) was extracted by hydro-distillation method using Clevenger apparatus. The oil thus extracted was used for in vitro antifungal efficacy against both the test fungi Curvularia trifolii and Rhizoctonia solani, using the poison food technique. The results show that the minimum inhibitory concentration (MIC) of the basil oil against C. trifolii 2000 ppm and R. solani was 1600 and however, it was cidal in nature at 3200 and 2400 ppm against both fungi, respectively. The current study’s findings show that O. sanctum oil could be an alternative to synthetic fungicides after detailed investigations.

YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea.

In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.

AnarMitra: Enhancing Pomegranate Farming with CNN Technology

Abstract: This study introduces AnarMitra, a mobile application designed to revolutionize pomegranate cultivation practices. AnarMitra employs machine learning algorithms to swiftly assess ripeness levels and detect prevalent diseases in pomegranates, such as bacterial blight and fungal infections. Utilizing smartphone cameras, farmers can effortlessly capture images for immediate feedback on fruit quality and health. The paper details AnarMitra's development process, including model training with diverse datasets and algorithmic methodologies for classification and disease detection. Field trials validate AnarMitra's efficacy in enhancing productivity and fostering sustainable agricultural practices, empowering farmers with timely insights for informed decision-making and crop management.

A Deep Learning Based Framework for Accurate Prediction of Disease of Crops and Nutrients in Smart Agriculture

Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combining computer vision, machine learning, and sensor technologies. The Raspberry Pi has many sensors to assess ambient and soil conditions in addition to a high-resolution camera for taking pictures. Using a machine learning model trained on a variety of datasets pertaining to agricultural illnesses and nutritional deficits, the system takes pictures of crop leaves and soil conditions. Preprocessing images, feature extraction, and convolutional neural network (CNN)-based categorization are important parts of our approach. The system can recognize evidence of nutrient deficits, such as nitrogen, phosphorus, and other deficiencies, as well as symptoms of a variety of crop illnesses, including fungal infections, bacterial blights, and viral diseases.

Detection of cotton leaf disease with machine learning model

This study aims to use a machine learning (ML) model to accurately classify four datasets of cotton crop leaves as either infected or healthy. Bacterial blight, Curly virus, Fussarium Wilt, and healthy leaves were used as the datasets for the study. ML is a useful tool in detecting cotton leaf diseases and can minimize the rate of disease. The problem is that without machine learning technique it is very difficult and time consuming to detect the diseases then to sort out this problem a machine learning model is proposed and to test the accuracy of the proposed model, the confusion matrix concept was used. The researchers have done their research works to diagnose the diseases by using (ML) model but the drawback of their research was that the results which were given by the different (ML) models were not accurate. The target of the study was to identify diseases affecting the cotton plant in the early stages using traditional techniques. However, utilizing various image processing techniques and machine learning algorithms, including a convolutional neural network, proved to be helpful in diagnosing the diseases. This technological approach can simplify the detection of damaged leaves and minimize the efforts of farmers in detecting those diseases. Cotton is a natural fiber produced on a large scale, and it is grown on 2.5% of overall agronomic land. The detection of cotton leaf diseases is crucial to maintain the crop's productivity and provide reliable earnings to farmers. A confusion matrix is N X N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by machine learning model. This technique has four parameters to test the accuracy of the results which is given in my research work.

Endophytic Fungi Residing within Cornus florida L. in Mid-Tennessee: Phylogenetic Diversity, Enzymatic Properties, and Potential Role in Plant Health.

Endophytic fungi that reside internally in healthy, asymptomatic plants often benefit their hosts by promoting plant growth and/or providing plant protection against abiotic and biotic stresses. However, only a small fraction of the estimated 1.5 million fungal endophytes have been identified. In this study, a total of 369 isolates of fungal endophytes in 59 distinct taxa were isolated from stem samples of Cornus florida (flowering dogwood). All isolates belonged to species of phyla Ascomycota and Basidiomycota distributed across five orders and 11 genera. Isolates belonging to the same family clustered together in a phylogenetic tree generated from a cluster analysis using MEGA 7 software. Diversity indices of the fungi revealed a rich and diverse community that included several species associated with leaf spots, blight, cankers, and/or dieback diseases. Pathogenicity tests confirmed 16 fungal endophytes as C. florida pathogens, including some well-known destructive pathogens Botryosphaera dothidea, Colletotrichum acutatum, and C. gleosporoides. Isolates of the fungal endophytes possess the capacity to produce extracellular hydrolytic enzymes (cellulase, amylase, pectinase, laccase, chitinase, and protease) that are known to function in tissue penetration, plant colonization, nutrient acquisition, and disease suppression in both plant pathogens and endophytes These results support the interchangeable pathogenic-endophytic roles for some taxa.

Convolutional Neural Networks Based Paddy Leaf Disease Detection

Abstract: This study proposes a novel approach for detecting paddy leaf diseases through the integration of Convolutional Neural Networks (CNNs). The process begins by converting input RGB images into HSV color space, where the saturation component is extracted. Subsequently, a binary image is generated, followed by background removal to enhance the accuracy of segmentation. The background-removed image is then converted back to the HSV color space for further processing. Kmeans segmentation is applied to segment the leaf regions effectively. The CNN architecture is employed for classification, categorizing the detected regions into various disease classes such as Healthy, Sheath Rot, Bacterial Leaf Blight, Brown Spot, and Leaf Blast. Additionally, a voice output feature is incorporated to provide audible feedback on the detected diseases. The study focuses on evaluating the accuracy of disease classification, aiming to demonstrate the effectiveness of the CNN approach in improving performance and reducing misclassifications compared to traditional methods. Overall, the proposed methodology offers a promising solution for accurate and efficient paddy leaf disease detection, with potential applications in agricultural management and crop protection

Rice Leaf Disease Detection using Deep Learning

Abstract: A wide range of bacterial, viral, or fungal diseases can damage rice leaves, significantly reducing rice yield. Finding the causes of rice ailments in the leaves is essential to feeding the world's massive population. However, the recognition of rice leaf rot depends on the settings and background of the image capture. Convolutional neural network (CNN) driven model is one of the most popular study topics in rice leaf disease detection. However, the existing CNN-based models only learn large network parameters and perform much worse in terms of recognition rates on different datasets. In order to detect rice leaf illnesses, we lower the network parameters and introduce a unique CNN-based method in this research. Using a special dataset of 4199 images ofrice leaf diseases, multiple CNN-based models are trained to identify five common rice leaf diseases. The recommended model achieves the best levels of 99.78% training accuracy and 97.35% validation accuracy. An independent set of pictures of rice leaf disease is used to evaluate the effectiveness of the proposed model, yielding the highest accuracy of 97.82% and an area under the curve (AUC) of 0.99. Additionally, based on binary classification trials, our proposed model achieves identification rates of 97%, 96%, 96%, 93%, and 95% for Blast, Brown spot, Bacterial Leaf Blight, Sheath Blight, and Turgor, respectively. These results demonstrate the effectiveness and superiority of our approach over the most sophisticated CNN-based algorithms for recognizing rice leaf