Classification Of Leaf Diseases Research Articles

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%.

A novel hybrid CNN methodology for automated leaf disease detection and classification

AbstractPlant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification

Paddy is a crucial food crop providing essential nutrients and energy and serving more than half the global population. Diagnosing and preventing plant diseases at an early stage is crucial for the health and productivity of crops. Automated disease diagnosis eliminates the need for experts and delivers accurate outcomes. This research will diagnose paddy leaf diseases with Deep Learning technology. The diseases such as bacterial blight, blast, tungro, brown spot, and healthy leaf classes are diagnosed and classified in this study. The dataset contains 160 images from each class with 800 images. Our proposed model is an ensemble of transfer-learned InceptionV3 and VGG16 architectures, which utilizes the strength of individual models to improve overall performance. The use of transfer-learned ensemble deep learning architectures achieved impressive accuracy rates of 97.03%, 94.97%, and 98.87% for training, validation and testing respectively. The results indicating that model is not overfit and generalizes well to unseen data. The model's performance is evaluated with confusion matrix with the parameters like precision, recall, F1-score, and support. We also tested the model's performance against other proposed deep learning techniques with and without transfer learning techniques. Moreover, this research advances reliable automated disease detection systems, fostering sustainable agriculture and enhancing global food security.

Optimization Based Rice Leaf Disease Classification in Federated Learning

Optimization Based Rice Leaf Disease Classification in Federated Learning

Statistical analysis and comparison of deep convolutional neural network models for the identification and classification of maize leaf diseases

Statistical analysis and comparison of deep convolutional neural network models for the identification and classification of maize leaf diseases

BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification

Accurate identification of apple leaf diseases is of great significance for improving apple yield. The lesion area of the apple leaf disease image is small and vulnerable to background interference, which easily leads to low recognition accuracy. To solve this problem, a lightweight bilinear convolutional neural network (CNN) model named BLSENet based on attention mechanism is designed. The model consists of two subnetworks, and each subnetwork is embedded with a Squeeze-and-Excitation (SE) module. By using the feature extraction ability of the two subnetworks and combining the bilinear feature CONCAT operation, the multiscale features of the image are obtained. Compared with the unimproved model LeNet-5 (84.63%), BLSENet has higher accuracy in the test set, which indicates that SE module and bilinear feature fusion have a positive effect on the performance of the model, and BLSENet has the ability to identify apple leaf diseases. The model has achieved the expected goal and can provide technical support for accurate identification and real-time monitoring of apple disease images.

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.

Deep hybrid classification model for leaf disease classification of underground crops

Underground crop leave disease classification is the most significant area in the agriculture sector as they are the significant source of carbohydrates for human food. However, a disease-ridden plant could threaten the availability of food for millions of people. Researchers tried to use computer vision (CV) to develop an image classification algorithm that might warn farmers by clicking the images of plant’s leaves to find if the crop is diseased or not. This work develops anew DHCLDC model for underground crop leave disease classification that considers the plants like cassava, potato and groundnut. Here, preprocessing is done by employing median filter, followed by segmentation using Improved U-net (U-Net with nested convolutional block). Further, the features extracted comprise of color features, shape features and improved multi text on (MT) features. Finally, Hybrid classifier (HC) model is developed for DHCLDC, which comprised CNN and LSTM models. The outputs from HC(CNN + LSTM) are then given for improved score level fusion (SLF) from which final detected e are attained. Finally, simulations are done with 3 datasets to show the betterment of HC (CNN + LSTM) based DHCLDC model. The specificity of HC (CNN + LSTM) is high, at 95.41, compared to DBN, NN, RF, KNN, CNN, LSTM, DCNN, and SVM.

Retracted: Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Retracted: Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Plant Leaf Disease Identification and Classification using Deep Learning Technique

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. However, detection of the kind of disease in a huge amount of crops is very difficult for farmers, and sometimes it becomes more difficult due to lake of knowledge about the disease since there are various kinds of diseases. The automated detection of crop disease with images has been done using many classification techniques, such as k- Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm, Support Vector Machine, and Main Component Analysis, Artificial Neural Network, and Fuzzy Logic. In this paper to improve detection capability, CNN has been used with an available database having sufficient knowledge of the disease. In this simulation, analysis of CNN-based leaf disease detection has been done for different values of learning rate and with different training algorithms. K-fold cross-validation has been used for the validation of the classifier. With this configuration, ~90% accuracy has been achieved

Correction: Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

Correction: Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

Automatic classification of ligneous leaf diseases via hierarchical vision transformer and transfer learning.

Identification of leaf diseases plays an important role in the growing process of different types of plants. Current studies focusing on the detection and categorization of leaf diseases have achieved promising outcomes. However, there is still a need to enhance the performance of leaf disease categorization for practical applications within the field of Precision Agriculture. To bridge this gap, this study presents a novel approach to classifying leaf diseases in ligneous plants by offering an improved vision transformer model. The proposed approach involves utilizing a multi-head attention module to effectively capture contextual information about the images and their classes. In addition, the multi-layer perceptron module has also been employed. To train the proposed deep model, a public dataset of leaf disease is exploited, which consists of 22 distinct kinds of images depicting ligneous leaf diseases. Furthermore, the strategy of transfer learning is employed to decrease the training duration of the proposed model. The experimental findings indicate that the presented approach for classifying ligneous leaf diseases can achieve an accuracy of 85.0% above. In summary, the proposed methodology has the potential to serve as a beneficial algorithm for automated detection of leaf diseases in ligneous plants.

Leaf disease classification with Multiple-model deep learning

The wilting of leaves caused by disease poses risks to both harvest yield and the environment. Therefore, the timely detection of disease signs on leaves is crucial to enable farmers to prevent disease outbreaks and safeguard their crops. However, manually observing all diseased leaves on a large scale demands substantial time and human effort. In this study, we propose an effective method for automated disease detection on leaves. Specifically, this method utilizes images captured from mobile phones. The proposed technique combines four models (ensemble of models) with distinct features: (1) ResNeXt50 model with a high-quality image processing, (2) ViT model with a low-quality image processing, (3) Efficientnet B5 model combines a self-learning with noisy input, and (4) Mobilenet V3 model with image segmentation. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods on TLU-Leaf dataset (ours) with F1-score of 90% and Cassava Leaf Disease dataset with F1-score of 87%.

Wheat leaf disease classification using modified ResNet50 convolutional neural network model

Wheat leaf disease classification using modified ResNet50 convolutional neural network model

An optimized capsule neural networks for tomato leaf disease classification

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research.

Rice Leaf Diseases Detection Using Machine Learning

One of India's most widely grown crops, rice is susceptible to a wide range of illnesses during the growing process. Due to a lack of training and experience, farmers have a hard time making reliable diagnoses when identifying these illnesses manually. Timely detection of diseases and the application of necessary treatments to afflicted plants are crucial for ensuring healthy and normal development of rice plants. In today's agricultural fields, the detection of leaf diseases is of the utmost importance. Consequently, we may use machine learning to identify diseases in rice leaves by image processing. The agriculture sector is in dire need of a system that can identify rice plant problems automatically. We present a novel convolutional neural network (CNN) model for the classification of prevalent rice leaf diseases. From a variety of picture backdrops and capture situations, our algorithm can identify rice leaf illnesses. Classifying disease pictures in rice leaves with complicated backgrounds and varying lighting conditions is our goal. We reach 95% accuracy with the CNNs based model. The outcomes for disease identification in rice demonstrate the effectiveness of suggested approach. Disease detection, CNN algorithm, rice leaf, and machine learning are index terms.

Lightweight Federated Transfer Learning for Plant Leaf Disease Detection and Classification across Multiclient Cross-Silo Datasets

Plant leaves and crops play a crucial role as a primary food source globally, making significant contributions to dietary iron intake (9%) and energy consumption (23%) per capita in the Asian region. Bacterial, yeast, and other microbial diseases pose significant challenges to farmers as they detrimentally impact plant health and reduce crop productivity. The manual diagnosis of these diseases poses a considerable challenge, particularly in regions with a scarcity of professionals specializing in leaves and crop protection. Automating leaf disease detection and providing easily accessible decision-support resources are crucial for facilitating efficient leaf protection strategies and mitigating crop damage. Despite multiple classification methods for diagnosing leaf diseases, a secure and accurate approach that fulfills these requirements has not yet been identified. This paper presents an architectural framework called Lightweight Federated Transfer Learning (LFTL) that addresses the challenge of Leaf Disease Detection and Classification (LDDC) while ensuring data privacy limitations are upheld. A dataset consisting of leaf disease images has been compiled, characterized by an imbalance in the distribution of the diseases. The collection includes four conditions: bacterial decay, brown spot, blast, and tungro, corresponding image counts of 1695, 1551, 1711, and 1419, respectively. Following the preprocessing stage, the LFTL framework was tested using both Independent and Identically Distributed (IID) and non-IID datasets. The study commenced with an efficacy evaluation of the Convolutional Neural Network (CNN) and eight TL models in the LDDC. The framework’s performance was evaluated across different circumstances and compared to conventional and federated learning models. The study’s findings revealed that the LFTL framework outperformed traditional distributed deep-learning classifiers, thus demonstrating its efficacy in individual and multiple client scenarios.

Advancements in Leaf Disease Classification: A Review of Deep Learning and Machine Learning Techniques

Advancements in Leaf Disease Classification: A Review of Deep Learning and Machine Learning Techniques

Field pea leaf disease classification using a deep learning approach.

Field peas are grown by smallholder farmers in Ethiopia for food, fodder, income, and soil fertility. However, leaf diseases such as ascochyta blight, powdery mildew, and leaf spots affect the quantity and quality of this crop as well as crop growth. Experts use visual observation to detect field pea disease. However, this approach is expensive, labor-intensive, and imprecise. Therefore, in this study, we presented a transfer learning approach for the automatic diagnosis of field pea leaf diseases. We classified three field pea leaf diseases: Ascochyta blight, leaf spot, and powdery mildew. A softmax classifier was used to classify the diseases. A total of 1600 images of both healthy and diseased leaves were used to train, validate, and test the pretrained models. According to the experimental results, DenseNet121 achieved 99.73% training accuracy, 99.16% validation accuracy, and 98.33% testing accuracy after 100 epochs. we expect that this research work will offer various benefits for farmers and farm experts. It reduced the cost and time needed for the detection and classification of field pea leaf disease. Thus, a fast, automated, less costly, and accurate detection method is necessary to overcome the detection problem.

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture