Left Images Research Articles

Leaf Disease Detection Using Convolutional Neural Network

Abstract: The aim of the project is to develop a Leaf disease detection system using deep learning, specifically Convolutional Neural Networks (CNNs). The project focuses on classifying images of plant leaves into 39 different disease categories by utilizing deep learning, specifically Convolutional Neural Networks (CNNs), for plant disease detection based on leaf images. The dataset consists of 39 classes with a totalof 61,486 images, and various augmentation techniques are applied to increase dataset size. The implementation involves PyTorch, with transformations for data augmentation, dataset creation usingImage Folder, and a split into training, validation, and testing sets. The CNN model is designed for image classification, using ReLU activation and soft max for the final layer. The training process involves batch gradient descent, and the model achieves an accuracy of 87% on training data, 84% on validation data, and 83% on test data. The key objectives include utilizing image data, implementing data augmentation techniques, creating a dataset, and training a CNN model to accurately predict and classify plant diseases based on input images. The ultimate goal is to provide a tool that can assist in early detection and diagnosis of plant diseases through automated analysis of leaf images

Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.

PLANT DISEASE RECOGNITION USING VGG-16

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

Deep Learning-Based Iris Recognition System Using Unprocessed Images

The iris image is a powerful and distinctive feature in biometrics that serves as a reliable instrument for human identification. Extracting significant features is crucial for developing iris-based recognition systems. While preprocessing for iris region detection is typically the first step in such systems, it can often fail in real acquisition conditions, leading to decreased performance. This study proposes a new iris-based recognition system that directly exploits original (noisy) images for feature extraction, avoiding the bottleneck of preprocessing. Additionally, a multimodal architecture is proposed for both right and left iris images to strengthen the decision step. Pretrained CNN models extract features classified by SoftMax and support vector machines (SVM). The performance of the proposed system is tested on four public datasets collected under different conditions. The results show that classification accuracy for the original iris dataset without pre-processing is higher than for the normalized database due to the rich CNN features that provide more cognitive information, resulting in greater discrimination power. The CNN multi-modal recognition system achieves the best accuracy compared to most state-of-the-art models, demonstrating the strength of the proposed fusion recognition system.

A Fresh Look at Tomato Leaf Disease Recognition using Vision Transformers

Tomatoes is one of the major economically significant vegetables produced worldwide, contributing greatly to increased agricultural production and food security. However, tomato plants are unfortunately prone to a number of diseases, including several that target the leaves, which can significantly reduce crop productivity and quality. Recently, deep learning techniques have revolutionized the fields of computer vision and image analysis. By automatically learning hierarchical representations from raw pixel data. Transformer is a new deep learning technique that opens new possibilities for image understanding using self-attention mechanisms to capture global dependencies within input sequences. This approach is exemplified by the Vision Transformer (ViT). In this study, we utilize and evaluate the effectiveness of six variations of the Vision Transformer (ViT) architecture in the task of tomato leaf disease recognition. The variants include Mobile ViT, EANet, Swin ViT, ViT, Shift ViT, and Compact ViT. Utilizing a publicly available, multiple-source dataset of tomato leaf images containing various disease patterns. Performance for all models was evaluated and compared in classifying various types of tomato leaf diseases in terms of accuracy, loss, precision, recall, and F1-Score, and the results showed that. The CompactVit has achieved the best accuracy of 97% and precision of 97% and 96% for recall. While the mobile ViT has the lowest performance among all variations in tomato disease recognition, overall, ViT is showing its promise, and it can be utilized on a large scale for smart agriculture, which opens the door for further exploration of this area.

The correlation between left atrium voltage mapping and 3D late gadolinium enhancement cardiac magnetic resonance imaging findings in patients with known atrial fibrillation

Abstract Introduction The non-invasive cardiac magnetic resonance imaging-based late gadolinium enhancement (LGE-CMR) of the left atrium (LA) fibrosis distribution and degree can be utilized for preprocedural planning and real-time guidance of AF ablation. There is an ongoing discussion with somewhat conflicting results regarding the accuracy of LGE-CMR of the LA as compared with the invasive electroanatomical mapping (EAM). Earlier data mainly encompassed post-ablation patients with an EAM involving a relatively small number of acquired points. Aim To evaluate the accuracy of LGE-CMR-based LA fibrosis using the ADAS 3D LA software among ablation-naive AF patients as compared with EAM using the CARTO-3 navigation system. Methods Patients with non-permanent AF, naive to previous ablation/cardiac surgery, who underwent CARTO-based AF ablation with preceding LGE-CMR scan were selected for the study. The 3D substrate atrial maps were generated based on the LGE-CMR (CMR-fib) using the ADAS 3D LA software and the CARTO EAM (EAM-fib). A fibrosis threshold of 1.2-1.32 was established for the CMR-fib using the Image Intensity Ratio whereas a threshold of 0.2-0.5mV was applied for the EAM-fib. The two 3D-maps were assessed for fibrosis using anatomically synchronized quantitative point by point comparison. The EAM was set as the gold standard. Results The study included 26,777 points acquired from 17 patients (aged 65.8±7.92 yrs with 6 females and 10 paroxysmal AF cases). CMR-fib showed 22.0% of LGE, while the EAM-fib demonstrated 48.8% of low voltage areas. The CMR-fib vs. EAM-fib point by point comparison concluded with an agreement of 73.1% and interrater reliability of 0.45 (p<0.001). The sensitivity and specificity were 45.0% and 99.9%, respectively, while the PPV and NPV were 99.8% and 65.6%, respectively. The Area Under the Curve (AUC) was calculated as 72.4. Increase of the EAM-fib threshold for scar (to 0.5-1.5mV) led to a further decrease in the assessment diagnostic performance and the interrater reliability. The lowest agreement was tested in the LA roof segments and the higher in the inferoseptal and posterior walls. Conclusion The LGE-CMR underestimates the presence of scar as presented by the EAM, however, those detected are mostly accurate. The low sensitivity that was demonstrated between the two methods may be attributed to the lower spatial resolution of the CMR as well as the superficial subendocardial layer mapped by EAM as compared with the averaged transmural mapping of the CMR-fib. A follow-up larger-scale study is required to assess the accuracy of LGE-CMR-based assessment of fibrosis, considering various wall thicknesses and establishing the scar threshold on the ADAS software, particularly for ablation-naive patients.

Tomato Leaf Disease Detection using Flask Frame Work

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Maize leaf disease recognition based on improved MSRCR and OSCRNet

Accurate identification of maize pests and diseases plays a crucial role in the growth and yield of maize. The recognition method of maize leaf diseases based on deep learning is currently a research hotspot. In practical applications, it is difficult to ensure a high recognition accuracy due to the impact of environmental factors (such as light intensity, background noise) and insufficient training data. To address this challenge, this paper proposes a maize image recognition method based on image enhancement and OSCRNet. Firstly, a maize leaf image enhancement framework and algorithm based on improved MSRCR were designed to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, the OSCRNet maize leaf recognition network model was established, which uses octave convolution with characteristics to accelerate network training and reduce unnecessary redundant spatial information in maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experimental results showed that the highest identification accuracy of the proposed method for rust, gray leaf disease, northern wilt disease, and healthy maize are 96.67%, 94.86%, 92.81%, and 95.63%, respectively. Our methods were beneficial in solving the problems of slow efficiency, low accuracy and insufficient training data, and also outperformed other comparison models. The proposed method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters. Also, this network structure has weak robustness with different parameters. Hence, in this manuscript, an Optimized DEDCNet (ODEDCNet) model is proposed for improved leaf disease image segmentation. To choose the best DEDCNet hyperparameters, a brand-new Dingo Optimization Algorithm (DOA) is included in this model. The DOA depends on the foraging nature of dingoes, which comprises exploration and exploitation phases. In exploration, it attains many predictable decisions in the search area, whereas exploitation enables exploring the best decisions in a provided area. The segmentation accuracy is used as the fitness value of each dingo for hyperparameter selection. By configuring the chosen hyperparameters, the DEDCNet is trained to segment the leaf disease regions. The segmented images are further given to the pre-trained Convolutional Neural Networks (CNNs) followed by the Support Vector Machine (SVM) for classifying leaf diseases. ODEDCNet performs exceptionally well on the PlantVillage and Betel Leaf Image datasets, attaining an astounding 97.33% accuracy on the former and 97.42% accuracy on the latter. Both datasets achieve noteworthy recall, F-score, Dice coefficient, and precision values: the Betel Leaf Image dataset shows values of 97.4%, 97.29%, 97.35%, and 0.9897; the PlantVillage dataset shows values of 97.5%, 97.42%, 97.46%, and 0.9901, all completed in remarkably short processing times of 0.07 and 0.06 seconds, respectively. The achieved outcomes are evaluated with the contemporary optimization algorithms using the considered datasets to comprehend the efficiency of DOA.

Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.

Unveiling silicon-mediated cadmium tolerance mechanisms in mungbean (Vigna radiata (L.) Wilczek): Integrative insights from gene expression, antioxidant responses, and metabolomics

Cadmium (Cd), one of the most phytotoxic heavy metals, is a major contributor to yield losses in several crops. Silicon (Si) is recognized for its vital role in mitigating Cd toxicity, however, the specific mechanisms governing this mitigation process are still not fully understood. In the present study, the effect of Si supplementation on mungbean (Vigna radiata (L.) Wilczek) plants grown under Cd stress was investigated to unveil the intricate pathways defining Si derived stress tolerance. Non-invasive leaf imaging technique revealed improved growth, biomass, and photosynthetic efficiency in Si supplemented mungbean plants under Cd stress. Further, physiological and biochemical analysis revealed Si mediated increase in activity of glutathione reductase (GR), ascorbate peroxidase (APX), and catalase (CAT) enzymes involved in reactive oxygen species (ROS) metabolism leading to mitigation of cellular damage and oxidative stress. Untargeted metabolomic analysis using liquid chromatography coupled with mass spectrometry (LC-MS/MS) provided insights into Si mediated changes in metabolites and their respective pathways under Cd stress. Alteration in five different metabolic pathways with major changes in flavanols and flavonoids biosynthesis pathway which is essential for controlling plants antioxidant defense system and oxidative stress management were observed. The information reported here about the effects of Si on photosynthetic efficiency, antioxidant responses, and metabolic changes will be helpful in understanding the Si-mediated resistance to Cd stress in plants.

Classification of mango disease using ensemble convolutional neural network

Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

Classification of Various Plant Leaf Disease Using Pretrained Convolutional Neural Network On Imagenet

Introduction/Background Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Leaf diseases impact agricultural production. Therefore, early detection and diagnosis of these diseases are essential. This issue can be addressed if a farmer can detect the diseases properly. Objective The fundamental goal of this project is to create and test a model for precisely classifying leaf diseases in plants. Materials and Methods This paper introduces a model designed to classify leaf diseases effectively. The research utilizes the publicly available PlantVillage dataset, which includes 38 different classes of leaf images, ranging from healthy to disease-infected leaves. Pretrained CNN (Convolutional Neural Network) models, including VGG16, ResNet50, InceptionV3, MobileNetV2, AlexNet, and EfficientNet, are employed for image classification. Results The paper provides a performance comparison of these models. The results show that the EfficientNet model achieves an accuracy of 97.5% in classifying healthy and diseased leaf images, outperforming other models. Discussion This research highlights the potential of utilizing advanced neural network architectures for accurate disease detection in the agricultural sector. Conclusion This study demonstrates the efficacy of employing sophisticated CNN models, particularly EfficientNet, to properly identify leaf diseases. Such technological developments have the potential to improve disease detection in agriculture. These improvements help to improve food security by allowing for preventive actions to battle crop diseases.

Feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm

In most data dimensionality reduction tasks, feature selection (FS) is an essential preprocessing stage. The slime mould algorithm (SMA) is a novel metaheuristic optimization algorithm. This paper proposes a feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm. This method is applied to the feature selection of banknote dirtiness recognition, and the Lévy flight operator is used to replace the control parameter vc in SMA, and a series of mathematical functions are added to replace the control parameter vb in the slime mould algorithm to enhance the global search capability. Test results show that this method achieves the highest accuracy of 76.85 % in recognizing the banknote dirtiness. Additionally, by applying the same method to feature selection and recognition of dirtiness for the left middle white area images of banknotes, the highest accuracy reaches 74.99 %. This suggests that the left middle white area can essentially replace the whole banknote. To verify the performance of the proposed feature selection method, comparative tests with four other optimization algorithms are carried out, and the results demonstrate that three improvement strategies can effectively improve the performance of SMA while maintaining a balance between exploration and exploitation. Tests have revealed that these three algorithms have significant advantages over the past SMA in terms of recognition accuracy and convergence speed when it comes to identifying the dirtiness of banknotes. Therefore, this method is considered to be one of the most effective methods to solve the feature selection problem of banknote dirtiness.

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization.

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

Medicinal Plant Classification and it's Identification

Indian Ayurvedic Medicine reached international level The basis of Ayurveda is herbal medicine. The pharmaceutical industry is starting to pay more. Be careful with herbs because their side effects and consequences are less than modern medicine and at the same time they are very expensive. The company efficiently and reliably uses leaf images from many deep and machine learning algorithms developed in recent years. In this study, leaves of 45 different medicinal plants were used and the deep learning method was used. A number of classification and identification methods were used using computer vision technology to achieve the best results. There are many medicinal plants according to the classification of the paper plant

Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm

The aim of this paper is to explore the importance of leaf wilting status detection and classification in agriculture to meet the demand for monitoring and diagnosing plant growth conditions. By comparing the performance of the traditional VGG16 image classification algorithm and the popular EfficientNet V3 algorithm in leaf image wilting status detection and classification, it is found that EfficientNet V3 has faster convergence speed and higher accuracy. As the model training process proceeds, both algorithms show a trend of gradual convergence of Loss and Accuracy and increasing accuracy. The best training results show that VGG16 reaches a minimum loss of 0.288 and a maximum accuracy of 96% at the 19th epoch, while EfficientNet V3 reaches a minimum loss of 0.331 and a maximum accuracy of 97.5% at the 20th epoch. These findings reveal that EfficientNet V3 has a better performance in leaf wilting status detection, which provides a more accurate and efficient means of plant health monitoring for agricultural production and is of great research significance.

REVOLUTIONIZING BIOLOGY LEARNING THROUGH AR: THE CASE OF LEAFCAPTURE APPLICATION DEVELOPMENT

REVOLUTIONIZING BIOLOGY LEARNING THROUGH AR: THE CASE OF LEAFCAPTURE APPLICATION DEVELOPMENT

Tomato Leaf Disease Detection System

Tomato is a widely cultivated crop with significant economic importance in the agro based industry. However, tomato plants are susceptible to various diseases that can severely impact yield and quality. Early and accurate detection of these diseases is crucial for effective disease management and ensuring optimal production. In this study, we propose a novel approach that a convolutional Neural Network (CNN) for the automated detection of tomato leaf diseases. First, Convolutions is employed to reduce the dimensionality of the input data, extracting the most relevant features for disease detection. The CNN leverages its ability to learn complex patterns and features from the data, enabling accurate classification of various tomato leaf diseases. To evaluate the effectiveness of our approach, we conducted experiments using a diverse dataset of tomato leaf images with different disease manifestations. Key Words: Agriculture, Tomato diseases, Leaf disease detection, Deep learning, neural network, Disease Diagnosis.

Plant Disease Detection Using Convolutional Neural Network

Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.