Plant Leaf Disease Detection Research Articles

Plant leaf disease detection using hybrid feature extraction techniques

Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery mildew, and leaf spot. Our research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques. We compiled sunflower leaf images from village databases and farm visits. Initially, color images were converted to grayscale, and the noise was reduced using Gaussian filtering during image pre-processing. Disease-affected areas were segmented using edge-based approaches. Hybrid feature extraction was then employed to encompass color and texture-related attributes. The K-Nearest Neighbors (KNN) algorithm facilitated disease classification. The proposed algorithm’s performance was benchmarked against SVM, RF and DT classifiers. Remarkably, our algorithm achieved exceptional accuracy: 98% for leaf blight, 97.3% for downy mildew, 95% for powdery mildew, and 96.5% for leaf spot detection. These results underscore the algorithm’s effectiveness in combating plant diseases and enhancing agricultural productivity.

Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches.

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.

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

Detection of plant leaf diseases using deep convolutional neural network models

Detection of plant leaf diseases using deep convolutional neural network models

Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI

Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI

Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review

Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Impact of Elevation Angle on Cross Polarization Discrimination for Telecommunication Application in Warri, Delta State, Nigeria

This study explores the impact of elevation angle on Cross Polarization Discrimination (XPD) in telecommunication systems, as evidenced by data collected under varying rain conditions. The study examines XPD values at elevation angles of 400 and 600 degrees across different rain classifications and intensities. Analysis of the data reveals that increasing the elevation angle generally leads to improvements in XPD, indicating enhanced discrimination against cross-polarized interference and improved signal quality. Notably, significant improvements in XPD are observed in conditions of widespread rain at higher elevation angles. However, the effectiveness of elevating antennas in mitigating interference diminishes as rain intensity increases, particularly in extreme cloudburst scenarios. The findings underscore the importance of considering elevation angle in antenna placement and system design to optimize XPD and ensure reliable communication performance, especially in adverse weather conditions. This research contributes to a deeper understanding of the complex relationship between elevation angle and XPD, offering valuable insights for the design and optimization of telecommunication networks.

Performance Analysis of Vector Machine Learning Algorithm based Plant Leaf Disease Detection and Soil Type Classification

The objective is to create and evaluate an image processing application designed for identifying leaf diseases and classifying soil types, utilizing a combination of Linear-Non-Linear (L-NL) Classification Techniques and the Vector Machine Learning (VML) Algorithm. We go over the methods created and put to the test for automating the process of classifying soil types and leaf diseases. The majority of methods begin by segmenting the measured signals from the acquired data using a segmentation algorithm. Next, effective extraction techniques are employed to extract the valuable information from these segments. Certain classifiers that are specifically designed to assign classes to these segments are constructed using the measured data and features that were extracted from the collected samples; these classifiers may make use of VML methods. Cone penetration testing collected data was utilised in the traditional approach for categorising subsurface soil, and positive outcomes were attained. In the past, classification systems required a domain expert to observe the trends and size of the segmented signals in addition to any accessible a priori knowledge. The suggested methods for employing image processing to automate this classification process are also included in this paper. Boundary energy approach can be used to extract the prominent aspects of the observed segments from a segmentation algorithm, for example. Dedicated linear and non-linear classifiers assign classes to the observed segments based on the measured data and attributes taken from the gathered data of soil and damaged and healthy plant leaves. Then, VML algorithm classification is applied. In summary, the procedure begins with the application of feature extraction methods in image processing to identify the characteristics of soil samples and leaf images. Subsequently, a database of these sample images is created. The classification of soil types and leaf diseases is then performed based on their vector features using the VML algorithm.

A novel rice plant leaf diseases detection using deep spectral generative adversarial neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

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 Convolutional Neural Network with Whale Optimization Algorithm (HCNNWO) Based Plant Leaf Diseases Detection

Plant diseases appear to be posing a serious danger to the production and availability of food globally. The main factor affecting the quality and productivity of agricultural products is the health of the plants. In this paper, we describe a modified plant disease detection using deep convolutional neural networks in real time. By employing image processing techniques to enlarge the plant illness photos, the plant disease sets of data were initially produced. To recognise plant illnesses, a system called Convolutional Neural Network combined with Wolf Optimisation algorithm (CNN-WO) was used. Finally, the Whale Optimization algorithm (WO) is used to maximise and optimizes getting input. And it is given to CNN's learning rate for classification process. This paper presents an image segmentation and classification technique to automatically identify plant leaf diseases. The suggested strategy increased accuracy, sensitivity, precision, F1 measure, and specificity of plant disease detection. According to this study, HCNNWO real detectors have improved, which would require deep learning. It would be an effective method for determining plant illnesses and other diseases within plants. According to the evaluation report, the suggested method offers good reliability. To evaluate how well the suggested algorithm performs in comparison to cutting-edge techniques such as SVM, BPNN and CNN, experiments are conducted on datasets that are openly accessible

Smart Agriculture Techniques for Plant Leaf Disease using AgriRobo

Machine learning is a branch of artificial intelligence (AI) and computer science. The primary causes of decreased agricultural production quality and quantity are Plant diseases. As plant structures and cultivation methods evolve, new diseases continue to emerge on plant leaves. Accurately classifying and detecting plant leaf diseases in their early stages restricts infection spread and promotes the healthy growth of plant production. Detecting plant diseases manually is a time-consuming and error- prone process. It can be an unreadable method of identifying and preventing the spread of plant diseases. The review resolves around two main axes: 1. Plant Leaf Disease Detection & Clas- sification. 2. Agrirobo Suggested disinfectant and automatically spraying for that plant leaf disease. Smart Agriculture to produce good crops quality and quantity without harming the healthy plant. A deep CNN (Convolutional Neural Networks) model captures the deep features while LBP (Local Binary Pattern) effectively extracts the local texture information.YOLO algorithm divides an image into the grid system and in that each grid detects objects within itself. This review paper explores the recent advancements and methodologies employed in this area, focusing on key techniques, challenges and emerging trends. Various image processing algorithms including Deep Convolutional Neural Net- works (DCNNs) and deep learning architectures, are discussed for their efficiency in automating plant leaves disease detection and classification

Implementation of Plant Leaf Disease Detection using K-means Clustering and Neural Networks

Plants exist all over the place; we live, as well as places without us. Plant disease is one of the essential causes that reduces quantity and degrades quality of the agricultural merchandises. Plant diseases have turned into a terrible as it can cause significant reduction in both quality and quantity of agricultural products. Images form important data and information in biological sciences. Until recently photography was the only method to reproduce and report such data. It is difficult to quantify or treat the photographic data mathematically. This project, classifies the plant leaves and stems at hand into infected and non-infected classes. The developing software provides a fast and accurate method in which the leaf diseases are detected and classified using k-means based segmentation and neural networks-based classification. Most common diseases seen in the leaves of Tapioca and Mango are discussed here for this approach. In this paper, respectively, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of diseases that effect on plant leaves. Recognizing the disease is mainly the purpose of the proposed approach. Thus, the proposed Algorithm was tested on five diseases which influence on the plants; they are: Early scorch, Cottony mold, ashen mold, late scorch, tiny whiteness. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of leaf diseases in a little computational effort. This project gives 95% of efficiency using MATLAB simulation results.

A PSO-CNN-based approach for Enhancing Precision in Plant Leaf Disease Detection and Classification

A PSO-CNN-based approach for Enhancing Precision in Plant Leaf Disease Detection and Classification

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.

Basil plant leaf disease detection using amalgam based deep learning models

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

Ensemble of CNN models for classification of groundnut plant leaf disease detection

Plant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehensive dataset of groundnut leaf images was created in collaboration with a pathologist, facilitating the automated identification of plant diseases. The field of image classification has found CNN to be quite effective. In this study, we create such a method for disease identification and classification by utilizing a tri-CNN architecture consisting of DenseNet169, Inception, and Xception —that have been pre-trained on the ImageNet dataset using two created non-linear equations on decision scores from the before mentioned base learners,the outlined ensemble methodology generates ultimate predictions for the test samples by incorporating the scores of four conventional metrics for evaluation, namely recall, precision, accuracy, and f1-score, obtained from the base learners. Hence mentioned CNN customized models are used to train our model for obtaining better results. The proposed approach evaluated on real world groundnut leaf dataset. The model that was put forth attained accuracy rates of 98.46 %.

A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity.