Leaf Disease Detection Research Articles

Image‐Feature Based Deep Learning Model for Plant Leaf Disease Detection

AbstractCurrently, everyone is facing significant difficulties with food scarcity. There may be several causes for this, but food loss is a well‐known issue. Specifically, crop losses bring on by plant and leaf diseases during farming operations. Plant disease is typically identified visually or through laboratory testing, which delays detection and reduces crop yields by the time it is finished. The wide use of smartphones along with recent advancements in computer vision has made it viable to diagnose any ailment by applying machine learning techniques. Smartphone‐assisted disease diagnosis is now a reality. In this study, a multi‐Support Vector Machine (SVM) model is used to detect four different diseases as well as healthy conditions of plant leaves. Using 13 feature vectors for each input, a total of 2400 leaves representing four distinct classes (four varieties) have been used to train and test the model. The experimental results give an average accuracy of 91.25% for diseased leaves detection and 99% accuracy for healthy leaves detection.

Detection of mango leaf disease using the convolution neural network method

Mango trees are a common plant in Indonesia. In 2021, 2.84 million tonnes of mangoes were produced in Indonesia, according to the Central Statistics Agency (BPS). You can eat mangoes either ripe or unripe. Additionally, this fruit can be turned into meals and beverages. Many farmers grow mango plants, however, Some illnesses can infect mangoes and lead to crop failure or poor fruit quality. Fungal infections like anthracnose and black fungus, often known as black fungus, affect mango plants, but many farmers continue to mistakenly believe they are identifying plant illnesses and pests. To the type of disease in mango plants, the Convolutional Neural Network (CNN) method was applied in this study. It has been demonstrated that CNN is a very efficient way of processing images and identifying key elements for intricate pattern recognition. A total of 1,405 leaf photos from three different categories—525 anthracnose images, 656 black sooty mold images, and 224 healthy images—were used as samples for CNN to identify illnesses in mango plants. This image data was taken from the kaggle.com website. The CNN model is trained using distinct datasets into training data and validation data after data collection and preprocessing. On training data, the model is 95% accurate, while on validation data, it is 98% accurate. By feeding photos of mango leaves into the model and evaluating the predictions, the detection is put into practice. Action can be taken to control the illness in these mango trees based on prediction findings showing the presence of disease with a decent amount of confidence

Analysis of SVM and KNN in the detection of leaf diseases

By utilizing the proposed automated strategy we can recognize the diseases without disappointment so we can lessen the use of manures by distinguishing the diseases. And along these lines we can lessen the wastage of cash and human exertion. The accuracy of the framework can be expanded by expanding the quantity of information base pictures. Here we got the greatest accuracy. It tends to be improved by expanding the pictures for every disease. Our efforts were focused on creating an automated method that could be used in agriculture to identify, count, categorize, and inspect mature or immature leaves. Over Leaf Diseases, the applied methodology was successfully implemented, and a satisfactory outcome was noted.

Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and to assess how well AI-based machine learning and deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study to obtain understanding of the current research trends, citation patterns, ownership and partnership arrangements, publishing patterns, and other parameters related to early identification of apple illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted to any solitary arena of research, despite the fact that there have been many studies on the identification of apple diseases. By employing a scientometric technique and 109 publications from the Scopus database published between 2011 and 2022, this study attempted to assess the condition of the research area and combine knowledge frameworks. To find important journals, authors, nations, articles, and topics, the study used the automated processes of VOSviewer and Biblioshiny software. Patterns and trends were discovered using citation counts, social network analysis, and citation and co-citation studies.

Rice leaf disease detection based on enhanced feature fusion and target adaptation

AbstractIntelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features.

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

Retracted: Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

Retracted: Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

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

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.

Detection of plant leaf diseases using deep convolutional neural network models

Detection of plant leaf diseases using deep convolutional neural network models

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection.

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

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.

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.

Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data. Our approach concatenates two pre-trained image classification deep learning-based models, DenseNet121 and MobileNetV2. We enhance DenseNet121 with an attention-based transition mechanism and global average pooling layers, while MobileNetV2 benefits from adding an attention module and global average pooling layers. We deepen the architecture with extra-dense layers featuring swish activation and batch normalization layers, resulting in a more robust and accurate model for diagnosing leaf-related plant diseases. LeafDoc-Net is evaluated on two distinct datasets, focused on cassava and wheat leaf diseases, demonstrating superior performance compared to existing models in accuracy, precision, recall, and AUC metrics. To gain deeper insights into the model's performance, we utilize Grad-CAM++.

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.

Potato Leaf Disease Detection Using Machine Learning

Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The dataset includes images of healthy leaves and leaves infected with different diseases such as early blight and late blight. The trained model is then used to classify new images of potato leaves into healthy or diseased categories. The proposed approach achieves 97.4% accuracy in the classification of potato leaf diseases such as early blight potato leaf disease and late blight potato leaf disease, and can be used as an effective tool for early detection and management of these diseases in potato crops.

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.

HYBRID DEEP LEARNING WITH ALEXNET FEATURE EXTRACTION AND UNET CLASSIFICATION FOR EARLY DETECTION IN LEAF DISEASES

This study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices.

Performance of Deep Learning Techniques in Leaf Disease Detection

Performance of Deep Learning Techniques in Leaf Disease Detection

Detection of Cocoa Leaf Diseases Using the CNN-Based Feature Extractor and XGBOOST Classifier

Detection of Cocoa Leaf Diseases Using the CNN-Based Feature Extractor and XGBOOST Classifier

Automated Tomato Leaf Disease Detection Using Image Processing: An SVM‐Based Approach with GLCM and SIFT Features

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.