Leaf Disease Research Articles

A Comparative Study and Optimization of Deep Learning Models for Grape Leaf Disease Identification

A Comparative Study and Optimization of Deep Learning Models for Grape Leaf Disease Identification

A Lightweight Real-Time Recognition Algorithm for Tomato Leaf Disease Based on Improved YOLOv8

To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model’s lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteristics of tomato leaf disease, which include irregular spot size and lush tomato leaves. The head network was redesigned utilizing partial and group convolution along with a parameter-sharing method. Scalable auxiliary bounding box and loss function optimization strategies were introduced to further enhance performance. After undergoing the pruning technique, computation decreased by 61.7%, the model size decreased by 55.6%, and the FPS increased by 44.8%, all while a high level of accuracy was maintained. A detection speed of 19.70FPS on the Jetson Nano was obtained after undergoing TensorRT quantization, showing a 64.85% improvement compared to the initial detection speed. This method met the high real-time performance and small model size requirements for embedded tomato leaf disease detection systems, indirectly reducing the energy consumption of online detection. It provided an effective solution for the online detection of tomato leaf disease.

Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification

Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.

Early Detection and Classification of Apple Leaf Diseases Using Deep Learning Technique

Apple leaf diseases represent a critical challenge for apple crop production, threatening both the quality and quantity of harvests. Leaves are essential to the growth and health of apple trees, as they play a pivotal role in photosynthesis and nutrient transport. When these leaves become infected, the diseases can rapidly spread across the entire tree, leading to significant damage that can impact the yield and the overall health of the orchard. The consequences of such outbreaks can be devastating, with severe economic losses for farmers and potentially reduced marketability of the crops. To mitigate these risks, timely and accurate detection of apple leaf diseases is crucial. Early intervention allows farmers to apply targeted treatments and prevent the spread of infection to other parts of the tree or adjacent trees in the orchard. This research proposes an improved deep learning approach, utilizing the VGG16 model, to detect early signs of apple leaf diseases. By focusing on small spots and widespread lesions, the model can identify symptoms at an early stage, allowing for prompt intervention to prevent further spread. The study employs a dataset from Kaggle and a custom repository called Apple Fungal Diseases Dataset, with the classification results compared to other leading techniques to ensure accuracy. By providing a reliable method for early detection, this research aims to support farmers in managing apple leaf diseases more effectively, ultimately contributing to healthier orchards and improved crop yields.

A novel system with robust compatibility and stability for detecting Sugarcane yellow leaf virus based on CRISPR-Cas12a.

Sugarcane yellow leaf virus (SCYLV) can reduce sugarcane productivity. A novel detection system based on reverse transcription-multienzyme isothermal rapid amplification (RT-MIRA) combined with CRISPR-Cas12a, named RT-MIRA-CRISPR-Cas12a, was developed. This innovative approach employs crude leaf extract directly as the reaction template, streamlining the extraction process for simplicity and speed. Combining RT-MIRA and CRISPR-Cas12a in one reaction tube increases the ease of operation while reducing the risk of aerosol contamination. In addition, it exhibits sensitivity equivalent to qPCR, boasting a lower detection limit of 25 copies. Remarkably, the entire process, from sample extraction to reaction completion, requires only 52-57 minutes, just a thermostat water bath. The result can be observed and judged by the naked eye.IMPORTANCESugarcane yellow leaf disease (SCYLD) is an important viral disease that affects sugarcane yield. There is an urgent need for rapid, sensitive, and stable detection methods. The reverse transcription-multienzyme isothermal rapid amplification combined with CRISPR-Cas12a (RT-MIRA-CRISPR-Cas12a) method established in this study has good specificity and high sensitivity. In addition, the system showed good compatibility and stability with the crude leaf extract, as shown by the fact that the crude extract of the positive sample could still be stably detected after 1 week when placed at 4°C. RT-MIRA-CRISPR-Cas12a, reverse transcription polymerase chain reaction (RT-PCR), and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were used to detect SCYLV on 33 sugarcane leaf samples collected from the field, and it was found that the three methods reached consistent conclusions. This Cas12a-based detection method proves highly suitable for the rapid on-site detection of the SCYLV.

EFS-Former: An Efficient Network for Fruit Tree Leaf Disease Segmentation and Severity Assessment

Fruit is a major source of vitamins, minerals, and dietary fiber in people’s daily lives. Leaf diseases caused by climate change and other factors have significantly reduced fruit production. Deep learning methods for segmenting leaf diseases can effectively mitigate this issue. However, challenges such as leaf folding, jaggedness, and light shading make edge feature extraction difficult, affecting segmentation accuracy. To address these problems, this paper proposes a method based on EFS-Former. The expanded local detail (ELD) module extends the model’s receptive field by expanding the convolution, better handling fine spots and effectively reducing information loss. H-attention reduces computational redundancy by superimposing multi-layer convolutions, significantly improving feature filtering. The parallel fusion architecture effectively utilizes the different feature extraction intervals of the convolutional neural network (CNN) and Transformer encoders, achieving comprehensive feature extraction and effectively fusing detailed and semantic information in the channel and spatial dimensions within the feature fusion module (FFM). Experiments show that, compared to DeepLabV3+, this method achieves 10.78%, 9.51%, 0.72%, and 8.00% higher scores for mean intersection over union (mIoU), mean pixel accuracy (mPA), accuracy (Acc), and F_score, respectively, while having 1.78 M fewer total parameters and 0.32 G lower floating point operations per second (FLOPS). Additionally, it effectively calculates the ratio of leaf area occupied by spots. This method is also effective in calculating the disease period by analyzing the ratio of leaf area occupied by diseased spots. The method’s overall performance is evaluated using mIoU, mPA, Acc, and F_score metrics, achieving 88.60%, 93.49%, 98.60%, and 95.90%, respectively. In summary, this study offers an efficient and accurate method for fruit tree leaf spot segmentation, providing a solid foundation for the precise analysis of fruit tree leaves and spots, and supporting smart agriculture for precision pesticide spraying.

Morphological, pathological and phylogenetic analyses identify a diverse group of Colletotrichum spp. causing leaf, pod and flower diseases on the orphan legume African yam bean

AbstractAfrican yam bean (AYB; Sphenostylis stenocarpa) is an underutilized legume indigenous to Africa with great potential to enhance food security and offer nutritional and medicinal opportunities. However, low grain yield caused by fungal diseases, including pod blight and leaf tip dieback, deters farmers from large‐scale cultivation. To determine the prevalence of fungal diseases affecting leaves, pods and flowers of AYB, a survey was conducted in 2018 and 2019 in major AYB‐growing areas in Nigeria. Leaf tip dieback, flower bud rot and pod blight were the most common symptoms. Morphological and molecular assays were conducted to identify the causal agents of the observed diseases. In all the samples examined, fungi from eight genera were isolated from diseased leaves, buds and pods. Koch's postulates were fulfilled only for fungi belonging to the Colletotrichum genus. Fungi from the other seven genera did not produce disease symptoms in healthy AYB tissues. Several Colletotrichum isolates were characterized by sequencing the rDNA internal transcribed spacer (ITS), glyceraldehyde‐3‐phosphate dehydrogenase, calmodulin and ApMAT loci. A combined phylogenetic analysis revealed four Colletotrichum species: C. siamense, C. theobromicola and C. fructicola, which were recovered from diseased leaves, and C. truncatum, recovered from diseased pods and buds. Our results are useful to gear efforts to develop integrated management strategies to control diseases affecting AYB in Nigeria and elsewhere. Availability of such strategies may stimulate greater AYB cultivation, which can contribute to diet diversification, something repeatedly advocated by a range of stakeholders to increase food security and prosperity of smallholder farmers.

Exploitation of the genetic potential of Thinopyrum and Agropyron genera to protect wheat from diseases and environmental stresses.

Common wheat is one of the most important food crops in the world. Grain harvests can be increased by reducing losses from diseases and environmental stresses. The tertiary gene pool, including Thinopyrum spp., is a valuable resource for increasing genetic diversity and wheat resistance to fungal diseases and abiotic stresses. Distant hybridization between wheat and Thinopyrum spp. began in the 1920s in Russia, and later continued in different countries. The main results were obtained using the species Th. ponticum and Th. intermedium. Additionally, introgression material was created based on Th. elongatum, Th. bessarabicum, Th. junceiforme, Agropyron cristatum. The results of introgression for resistance to diseases (leaf, stem, and stripe rusts; powdery mildew; Fusarium head blight; and Septoria blotch) and abiotic stresses (drought, extreme temperatures, and salinity) to wheat was reviewed. Approaches to improving the agronomic properties of introgression breeding material (the use of irradiation, ph-mutants and compensating Robertsonian translocations) were described. The experience of long-term use in the world of a number of genes from the tertiary gene pool in protecting wheat from leaf and stem rust was observed. Th. ponticum is a nonhost for Puccinia triticina (Ptr) and P. graminis f. sp. tritici (Pgt) and suppresses the development of rust fungi on the plant surface. Wheat samples with the tall wheatgrass genes Lr19, Lr38, Sr24, Sr25 and Sr26 showed defence mechanisms similar to nonhosts resistance. Their influence led to disruption of the development of surface infection structures and fungal death when trying to penetrate the stomata (prehaustorial resistance or stomatal immunity). Obviously, a change in the chemical properties of fungal surface structures of races virulent to Lr19, Lr24, Sr24, Sr25, and Sr26 leads to a decrease in their adaptability to the environment. This possibly determined the durable resistance of cultivars to leaf and stem rusts in different regions. Alien genes with a similar effect are of interest for breeding cultivars with durable resistance to rust diseases and engineering crops with the help of molecular technologies.

Image classification of leaf disease in corn plants (Zea Mays L.) using the MobileNetV2 method

One of the main problems leading to low yields and possible crop failure in maize, a crop of great importance to human civilization, is that plant diseases are discovered and treated too late, leading to more severe diseases and even crop failure. Using photos taken from the Kaggle platform and some field shots, this research seeks to develop a classification system that can identify different types of diseases present on maize leaves. The disease types identified include Common Rust, Gray Leaf Spot, and Bacterial Leaf Blight. MobileNetV2 uses a Convolutional Neural Network (CNN) design to handle resource-intensive processes. To produce a lightweight model, this CNN uses separate corner shifts. The dataset for this study was taken from field shooting and the Kaggle platform. The study found that the MobileNetV2 model clarified objects very well with 93.01% accuracy. This discovery will help farmers find diseases on corn leaves (Zea Mays L.).

A Robust Solution for Tea Leaf Classification using the SMOTE and Soft Voting Classifier

Background/Objectives: The purpose of the proposed work is to identify how a tea leaf is conceptualized. This further enables the categorization of leaves into healthy and unhealthy groups, assisting both the vendor and customer in obtaining the right quality for their requirements. The primary cause of the low production of tea in various parts of the country is the late detection of diseased leaves. Literature suggests that the current AI techniques can detect but have a high level of similarities. Hence, the objective is to identify diseased leaf images at early stage. Methods: Earlier studies have used mostly CNN for classification which is more time consuming, and they create their own parameters. In this study, we extracted the textual features of images by using GLCM, and further classification was implemented with the help of SMOTE and a soft voting classifier. Findings: The proposed method resulted in optimal timeline without using CNN for classification. The accuracy is better when compared to current methods. Novelty: This work presents a novel approach in calculating textual features of leaves that can be further used for classification or tea-leaf grading purposes. Keywords: Image processing, Feature extraction, GLCM, SMOTE, Voting classifier

Bangladeshi crops leaf disease detection using YOLOv8

The agricultural sector in Bangladesh is a cornerstone of the nation's economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly vulnerable to various leaf diseases, which pose significant threats to crop yields and food security if not promptly addressed. Consequently, there is an urgent need for an automated system that can accurately identify and categorize leaf diseases, enabling early intervention and management. This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You Only Look Once), in surpassing previous models for the automated detection and categorization of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the goal is to enhance the efficiency of disease detection and management. A dataset comprising 19 classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple objects simultaneously, was employed to train a deep neural network. The system's performance was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this approach to enhance crop disease management, thereby improving food security and promoting agricultural sustainability in Bangladesh.

FSM-YOLO: Apple leaf disease detection network based on adaptive feature capture and spatial context awareness

Apple leaf disease is a key factor affecting apple yield. Detecting apple leaf diseases in unstructured environments presents a significant challenge due to the diverse early forms and varying scales of the diseases, as well as the similarity between the diseased areas and the background. To address these challenges, this paper proposes an improved convolutional neural network FSM-YOLO with adaptive feature capture and spatial context awareness. Firstly, to address the lack of feature extraction due to the complex texture structure of disease features, AFEM (Adaptive Feature Enhancement Module) with the ability of contextual information fusion and channel information modulation is proposed, which enhances the feature extraction capability for multiple disease types. Secondly, SCAA (Spatial Context-aware Attention) module with spatial relationship capture and adaptive receptive field adjustment was designed to enhance the network's ability to spatial relationship modeling and its ability to focus on disease characteristics to distinguish between disease targets and background information. Finally, MKMC (Multi-kernel mixed Convolution) is proposed to enhance multi-scale feature extraction capability by efficiently capturing and integrating information at multiple spatial resolutions to cope with different scales and shape variations of early leaf disease types. Experiments were conducted on an apple leaf disease dataset covering eight different disease types with 15,159 disease instances, and the experimental results show that compared with the baseline model YOLOv8s, FSM-YOLO improves mAP@0.5 by 2.7%, precision by 2.0%, and recall by 4.0%. Meanwhile, experimental results on the open-source apple leaf disease dataset ALDOD and plant leaf disease dataset PlantDoc show that FSM-YOLO outperforms the state-of-the-art algorithms, which validates the versatility of FSM-YOLO and confirms its excellent detection performance in various plant disease scenarios.

A survey of detecting leaf diseases using machine learning and deep learning in various crops

<p><span lang="EN-US">For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.</span></p>

Remora‐CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification

ABSTRACTFor millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the Remora Optimization Algorithm (ROA). A better classification outcome is attained by performing the segmentation process using K‐means with the Fractional Tangential‐Spherical Kernel (FTSK) algorithm. Furthermore, the developed Remora Optimization‐ Convolutional Neural Network (Remora‐CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset.

Enhancing leaf disease detection accuracy through synergistic integration of deep transfer learning and multimodal techniques

The agricultural sector, a cornerstone of economies worldwide, faces significant challenges due to plant diseases, which severely affect crop yield and quality. Early and accurate detection of these diseases is crucial for effective mitigation strategies. The current methods used often lack accuracy and adaptability, especially in diverse environmental conditions. This study introduces a novel, synergistic approach that integrates deep transfer learning with multimodal techniques, specifically canny edges, colour spectrum intensity analysis, and custom data augmentation strategies. Unlike existing methods that rely solely on pre-trained models, the approach utilised in this study offers an innovative fusion of distinct feature extraction techniques. The canny edges highlighted the structural intricacies of leaf diseases, while colour spectrum intensity analysis enhanced the detection of disease-specific colour markers. The customized data augmentation techniques employed (in the study) was shown to enhance the learning process of the models, resulting in their adaptability to diverse agricultural environments. This integration applied to DenseNet201 and EfficientNetB3, achieved detection accuracies of 99.03 % and 98.23 %, respectively, surpassing traditional models and setting new benchmarks in plant disease detection. These results demonstrate the effectiveness of the proposed multi-faceted approach and its potential to significantly enhance crop disease management systems.

Review of image processing and artificial intelligence methodologies for apple leaf disease diagnosis

<span lang="EN-US">Apple leaf disease (ALD) potentially affects the apple tree's health by reducing fruit yield and its capability to grow healthy. The prime purpose of the proposed study is to review and assess the strengths and weaknesses associated with the frequently exercised methods of ALD diagnosis using image processing and artificial intelligence (AI). Although these are widely adopted in recent studies, the core notion is to find the pros and cons associated with the practical viability. A desk research methodology is undertaken to carry out proposed review work where a database of recent scientific manuscripts is collected and studied very closely. The existing approaches are reviewed concerning identified problems, adopted solutions, advantages, and limitations. Finally, the paper contributes towards offering insight into potential research gap which will guide the upcoming researchers to make wise decisions for planning their models. The results acquired from this review work show that generalized challenges of ALD are not addressed, less emphasis on illumination variability, reduced target to minimize complexity, lesser evidence towards real-time processing, no evidence towards interpretability, limitation of available dataset, and tradeoff-between image processing and AI.</span>

Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2

Paddy farming, a cornerstone of global agriculture, faces significant threats from various diseases that affect the crop yield. This research presents a novel approach for detecting paddy leaf diseases using advanced deep learning techniques, specifically transfer learning with the MobileNetV2 architecture. The methodology involves the utilization of a comprehensive dataset consisting of paddy leaf images across multiple disease classes. Data augmentation was extensively employed to address the limitations posed by the dataset size. Both basic and advanced models were trained, with the advanced model achieving a remarkable validation accuracy of 97%. Additionally, Time-Test Augmentation (TTA) was applied to further enhance the model's performance. This research demonstrates the efficacy of deep learning techniques in agricultural disease detection and highlights potential improvements for future applications.

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases.

An efficient corn leaf disease prediction using Adaptive Color Edge Segmentation with Resnext101 model

Corn leaf disease prediction is essential in ensuring agricultural productivity and food security. Although classical segmentation and classification techniques have been used to identify diseases, they often fail to deal with complex leaf structures. Thus, deep learning has attracted growing attention for automatically learning discriminative features from data. This paper proposes a new approach to predicting corn leaf disease using deep learning and classical segmentation techniques for improved accuracy and efficiency. The proposed work presents an adaptive colour edge segmentation method improved by a hill-climbing search for robust feature extraction according to the diversified nature of corn leaves. With the help of stochastic hill climbing, the segmentation process is optimized by adaptively changing parameters to get better segmentation accuracy. After that, features are extracted and provided as input into a ResNeXt-101 model for disease classification. The novel activation function in ResNeXt-101 combines the Adam optimizer with a Gaussian-based GELU to enhance nonlinearity to the smooth activation of GELU. The results obtained from experiments show that the proposed approach is practical. The ResNeXt-101 model has achieved overwhelming performance, with 99% for testing accuracy and a minimum loss of 0.01, proving its superior generalization capability and robustness. The proposed work was performed with no overfitting and free from local minima during the training process.

Machine Learning Techniques for Medicinal Leaf Prediction and Disease Identification

Trees have been a crucial component in humans' lives for hundreds of years, providing food, shelter, and medicine. Some trees have a lot of medicinal properties that cure many diseases. In the old days, Ayurvedic methods were popular for various treatments, but nowadays, the demand for foreign medicine is increasing gradually, which also has side effects. This paper addresses this issue by deciding on the medical conditions corresponding to a symptom and predicting an herb leaf that can be treated it using some modern machine learning techniques. We have used three machine learning methods to accomplish this goal: Multinomial Naive Bayes, Gradient Boosting and Random Forest. These techniques were then used to assess the symptoms and decide the name of the disease and which leaf is appropriate for medicine. The highest accuracy (92%) was produced by the Multinomial Naive Bayes algorithm, thereby showing its capability to predict the right medicinal leaf based on given symptoms. The results show that machine learning algorithms, especially Multinomial Naive Bayes, can identify diseases and recommend suitable medicinal leaves. This approach holds promise for integrating traditional Ayurvedic knowledge with modern technology to offer alternative treatments with potentially fewer side effects.