Classification Of Plant Leaf Diseases Research Articles

Semantic segmentation for plant leaf disease classification and damage detection: A deep learning approach

Agriculture sustains the livelihoods of a significant portion of India's rural population, yet challenges persist in manual practices and disease management. To address these issues, this paper presents an automated plant leaf damage detection and disease identification system leveraging advanced deep learning techniques. The proposed method consists of six stages: first, utilizing YOLOv8 for region of interest identification from drone images; second, employing DeepLabV3+ for background removal and facilitating disease classification; third, implementing a CNN model for accurate disease classification achieving high training and validation accuracies (96.97 % and 92.89 %, respectively); fourth, utilizing UNet semantic segmentation for precise damage detection at a pixel level with an evaluation accuracy of 99 %; fifth, evaluating disease severity; and sixth, suggesting tailored remedies based on disease type and damage state. Experimental analysis using the Plant Village dataset demonstrates the effectiveness of the proposed method in detecting various defects in plants such as apple, tomato, and corn. This automated approach holds promise for enhancing agricultural productivity and disease management in India and beyond.

An Improve Method for Plant Leaf Disease Detection and Classification using Deep Learning

In countries like India, whose important occupation is agriculture, face a huge loss when the crops get affected by any type of disease. These diseases attack the crops in various stages and can destroy the entire production. Since most diseases are transmitted from one crop to another there is an essential requirement to detect the type of disease in the early stage so that farmers can take the required action to “save the crops” and production. Early disease detection is one of the essential activities for enhancing agricultural productivity. Diseases spread very quickly in the parts of the leaves that affect the growth of the plants. Early detection is a challenging task as the symptoms are mild for accurate identification. This research paper presents an enhanced CNN based MCC-ECNN model with fine-tuned hyper-parameters and various batch sizes for accurate plant leaf disease classification.

Plant leaf disease classification using FractalNet

In this work, an effort is made to apply the FractalNet model in the field of plant disease classification. The proposed model was trained and tested using a “PlantVillage” plant disease image dataset using a central processing unit (CPU) environment for 300 epochs. It produced an average classification accuracy of 99.9632% on the test dataset. The experimental results demonstrate the efficiency of the proposed model and show that the model achieved the highest values compared to other deep learning models in the PlantVillage datasets.

Using Optuna and Genetic Algorithm together facilitate the classification of plant leaf diseases

The identification and severity assessment of plant leaf diseases is crucial to food security and sustainable agriculture. This study shows an innovative way to improve plant leaf disease detection. The recommended method uses Optuna for parameter optimization and the Genetic Algorithm for feature selection to improve plant leaf disease identification. We do this to improve diagnosis accuracy. This method improves classification accuracy and is called ECPLDD-OGA. Modern hyper parameter optimization framework Optuna is employed. This allows classification model parameters to be fine-tuned. A systematic feature selection method is the Genetic Algorithm. It finds the most useful characteristics in the input dataset. By applying the algorithm on the data. By facilitation, the iterative process helps create a simplified and meaningful subset of features. Contrary to parameter tinkering and feature selection, empirical data suggests that utilizing Optuna and the Genetic Algorithm simultaneously improves disease identification. The updated model recognizes sick plants more accurately and generalizes better. Optimization enabled both gains. The usage of this technology can improve agricultural operations and reduce crop losses by increasing productivity. The present ECPLDD-OGA technique helps integrate hyper parameter tweaking and feature selection into machine learning-based agricultural applications.

An intelligent deep residual learning framework for tomato plant leaf disease classification

Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.

Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

Classification of plant leaf diseases using deep neural networks in color and grayscale images

For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

SMoGW-based deep CNN: Plant disease detection and classification using SMoGW-deep CNN classifier

Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.

Fusion of AI Techniques: A Hybrid Approach for Precise Plant Leaf Disease Classification

Classification of plant leaf diseases is an important step in protecting the world's food supply and agricultural yields. There has been encouraging progress in improving the efficiency and accuracy of plant leaf disease diagnosis via the combination of deep learning methods with artificial intelligence (AI) in recent years. This research introduces a new hybrid strategy, CNN+SVM and CNN+RF, that uses deep learning techniques like Convolutional Neural Networks (CNN) in conjunction with more traditional machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM). Moreover, two hybrid variants, CNN + RF and CNN + SVM, are proposed to exploit the strengths of both paradigms synergistically. To further improve classification accuracy, the study employs Particle Swarm Optimization (PSO) as a feature selection technique. PSO optimizes the feature subset for each classification model, facilitating the extraction of the most informative features, which leads to better discrimination between healthy and diseased plants. The dataset used for experimentation consists of a comprehensive collection of plant leaf images representing various diseases across multiple plant species. Experimental results demonstrate the efficacy of the proposed hybrid approach compared to individual classification methods. The hybrid models achieve higher accuracy rates and improved generalization performance, showcasing the synergistic benefits of combining AI and deep learning techniques. Furthermore, the feature selection process through PSO contributes significantly to enhancing the classification outcomes, providing insights into the discriminative power of selected features. This research contributes to the advancement of plant leaf disease classification methodologies by offering an innovative hybrid approach that leverages the complementary strengths of AI, deep learning, and feature selection techniques. The study's findings underscore the potential for improving plant leaf disease management strategies, ultimately leading to enhanced crop productivity and sustainable agriculture. The proposed hybrid framework can serve as a blueprint for similar classification tasks in other domains, demonstrating the broader impact of synergizing different AI techniques for improved accuracy and performance. CNN+RF gives 95% accuracy, 93% precision, 96% recall and 94% F1 score, whereas CNN+SVM gives 93% accuracy, 91% precision, 94% recall and 92% F1 score.

RETRACTED ARTICLE: Feature selection with Optimized XGBoost model-based paddy plant leaf disease classification

RETRACTED ARTICLE: Feature selection with Optimized XGBoost model-based paddy plant leaf disease classification

ERCP-Net: a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network

Plant leaf diseases are a major cause of plant mortality, especially in crops. Timely and accurately identifying disease types and implementing proper treatment measures in the early stages of leaf diseases are crucial for healthy plant growth. Traditional plant disease identification methods rely heavily on visual inspection by experts in plant pathology, which is time-consuming and requires a high level of expertise. So, this approach fails to gain widespread adoption. To overcome these challenges, we propose a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network (ERCP-Net). It consists of channel extension residual block (CER-Block), adaptive channel attention block (ACA-Block), and bidirectional information fusion block (BIF-Block). Meanwhile, an application for the real-time detection of plant leaf diseases is being created to assist precision agriculture in practical situations. Finally, experiments were conducted to compare our model with other state-of-the-art deep learning methods on the PlantVillage and AI Challenger 2018 datasets. Experimental results show that our model achieved an accuracy of 99.82% and 86.21%, respectively. Also, it demonstrates excellent robustness and scalability, highlighting its potential for practical implementation.

Betel Leaf Diseases Classification using Machine Learning Algorithm: A Feasible Approach

Even though the development in science and technology in various domains, agriculture is essential for humans to survive. It is mandatory to implement the development of science in the agriculture sector. There is a huge demand for agricultural workers because of lots of risk in production behind productivity. One of the major risks is diseases caused in plants. Our approach uses region of interest algorithms for image segmentation and feature extractions are done by GLCM and finally the diseases are classified with the help of ELM algorithm. These improvements in the classification of plant leaf diseases not only increase the precision and effectiveness of disease diagnosis, but also make it easier to accurately monitor vast fields of crops quickly. Due to its ability to precisely diagnose and categorize plant leaf diseases, the extreme learning machine technique is a novel strategy that has attracted a lot of attention. Additionally, this method has benefits over conventional image processing methods, which could find it difficult to categorize various plant leaf diseases. Research on betel leaf has received a lot of attention because of its possible medical benefits. Studies on its impact on dental health have been done because betel leaf extracts have antibacterial and anti-inflammatory properties. Approximately 1047 betel leaf images are captured to test our approach by taking five classes of diseases like Anthracnose, Phytophthora Foot Rot, Fusarium wilt, Bacterial Leaf Rot, Powdery Mildew. The model reports accuracy of 97%.

Efficient deep learning architecture for the classification of diseased plant leaves

<span>The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy.</span>

ZS-DML: Zero-Shot Deep Metric Learning approach for plant leaf disease classification

ZS-DML: Zero-Shot Deep Metric Learning approach for plant leaf disease classification

Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification

Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification.

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset's mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA's performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.

Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

An Effective Data Augmentation Based on Uncertainty Based Progressive Conditional Generative Adversarial Network for improving Plant Leaf Disease Classification

An Effective Data Augmentation Based on Uncertainty Based Progressive Conditional Generative Adversarial Network for improving Plant Leaf Disease Classification

Adaptive feature selection with deep learning MBi-LSTM model based paddy plant leaf disease classification

Adaptive feature selection with deep learning MBi-LSTM model based paddy plant leaf disease classification

An efficient banana plant leaf disease classification using optimal ensemble deep transfer network

ABSTRACT Plants are a major source of food all around the world, and they are mainly affected by diseases caused by pathogens, insects, and parasitic plants. If the diseases are identified in the earlier stage, then it will be easy to apply pesticides and prevent the disease from further propagation. To recognise these diseases at earlier stages automatically, different researchers have established artificial intelligence-based approaches using machine learning and deep learning approaches which can identify the diseased lesions with high accuracy. In this paper, a novel Hybrid Moth Flame Optimization Algorithm-butterfly optimisation algorithm (HMFO-BOA) based optimal ensemble deep transfer network (OEDTN) classifier for banana leaf disease identification is developed. The OEDTN architecture offers increased predictability for banana leaf disease using Maximum Mean Discrepancy (MMD), ensemble learning, domain adaptation, and parameter transfer learning. The feature extraction is done by constructing different Deep Transfer Networks (DTN) via diverse kernel MMD. Finally, the DTNs are integrated via ensemble learning to obtain the final classification outcomes. The MFOBOA algorithm assigns optimal voting weights for each DTN to dynamically construct the OEDTN architecture. In the consequent testing process, the input banana leaf disease images are categorised into diverse classes such as BBW (Banana Bacterial Wilt Disease), BBS (Banana Black Sigatoka Disease), Cordana, pestalotiopsis, Sigatoka, and healthy. The experiments conducted on the Banana Leaf and BBW-BBS datasets prove that the proposed model offers improved performance than the state-of-art techniques.