Identification Of Leaf Diseases Research Articles

A Novel Deep Convolutional Neural Network Approach using Jacobi Polynomial and Laplacian Function (JPLF) in Recognition of Plant Leaf Disease

Background/Objectives: Enhancing agricultural productivity is crucial for fostering economic growth. Plant diseases significantly threaten crops, necessitating timely detection to mitigate adverse impacts on quality, quantity, and overall productivity. This research addresses the importance of early disease detection in agriculture and proposes an innovative method utilizing Jacobian Polynomial and Laplacian Function for precise identification. Methods: Efficient monitoring of large-scale crop farms with minimal workforce is essential. To achieve this, an automatic method for plant disease detection is proposed. The method leverages Jacobian polynomials to expand input features, mitigating correlation issues among input vectors. The expanded Jacobi polynomial is the input vector for a backpropagation algorithm with a novel activation function based on the Laplacian function. Findings: The efficacy of the proposed JPLF model is demonstrated through the accurate identification of leaf diseases, achieving a high testing accuracy of 92.07%. Comparative analysis with existing models, such as CNN with MobileNet V2 (85.38%) and the IoU model (83.75%), highlights the superiority of the JPLF model in plant disease detection. Novelty: To overcome the limitations of existing approaches, the incorporation of Jacobian polynomials plays a pivotal role in expanding input features. This expansion aids in eliminating correlations among input vectors, enhancing the efficacy of disease detection. The proposed model, Jacobi Polynomial and Laplacian Function (JPLF) introduces a unique activation function based on the Laplacian function, improving accuracy. Keywords: Plant Disease Detection, Jacobi Polynomial, Laplacian Transform, Deep Learning Model, Feature Expansion

Empirical Analysis of Squeeze and Excitation-Based Densely Connected CNN for Chili Leaf Disease Identification

Empirical Analysis of Squeeze and Excitation-Based Densely Connected CNN for Chili Leaf Disease Identification

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

Design and Implementation of Apple Leaf Disease Recognition System Based on ResNet50

Timely identification of apple leaf diseases is critical to preventing crop losses and safeguarding yields. Spotted leaf drop, brown spot, gray spot, mosaic and rust are all common types of apple leaf diseases, and their presence signals a potential risk that could lead to significant reductions in fruit and crop yields. The apple industry is thus at risk of economic losses. In order to solve this problem, this study applies ResNet50, a deep learning model for image recognition and classification of apple leaf diseases, and develops an intelligent recognition system for apple leaf diseases by combining PyQt technology. The purpose of this system is to overcome the shortcomings of traditional recognition methods. Through experimental validation, the ResNet50 model achieves a high accuracy rate of 93.19% in apple leaf disease recognition, demonstrating its efficiency and practicality in practical applications.

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat

Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat

Apple leaf diseases recognition based on ResNet-101 and CBAM

Early identification and management of plant diseases is crucial to raising crop quality and yield. In order to create an effective deep learning model for autonomous apple leaf diseases identification, the issue of recognizing and classifying apple leaf infections is the main focus of this investigation. To improve the detection performance of the model, Convolutional Block Attention Module (CBAM) is added to the ResNet-101 deep convolutional neural network (CNN) model utilized in this work. According to the experimental findings, the ResNet-101 model with Convolutional Block Attention Module exhibits excellent performance when attempting to diagnose problems in apple leaves. Specifically, the ResNet-101+CBAM model outperforms the standard ResNet-101 model in the disease categories of 'Brown spot', 'Grey spot', 'Health' and 'Rust'. In some disease categories, such as 'Powdery mildew', the performance enhancement of the module (CBAM) is relatively insignificant due to the confusion caused by the similarity of features between the categories. Further analysis showed that the data enhancement technique had a significant positive impact on model performance.

CASF-MNet: multi-scale network with cross attention mechanism and spatial dimension feature fusion for maize leaf disease detection

Maize is an important food crop, and accurate identification of maize leaf diseases is significant for disease control. Aiming at the complexity of the color and texture features of diseases with inter-class similarity and intra-class variability, a two-channel multi-scale network model with a cross-attention mechanism and spatial dimension feature fusion (CASF-MNet) was designed. Firstly, aiming at the complexity of color features, the HSV color Sub-network (CSNet) was designed to extract the color features of disease spots. Then, aiming at the complexity of texture features, Texture Sub-Network (TSNet) and Cross Attention mechanism (CAM) were proposed to enhance the texture features of disease spots. Finally, to better fuse color and texture features, a cross-spatial feature fusion mechanism (FFM) was proposed. In the five-fold cross-validation of the self-built dataset, the average F1-Score and average Accuracy of CASF-MNet reach 97.17% and 97.02%. In the test experiments of two public datasets, the F1-Score of CASF-MNet reaches 98.13% and 98.71%, and the Accuracy reaches 97.44% and 98.67%, which are better than the seven most advanced leaf disease identification methods. It is proved that CASF-MNet is advanced and stable, and can assist disease detection personnel in maize disease detection in field environments.

Deep Learning Techniques for Identification of Different Malvaceae Plant Leaf Diseases

INTRODUCTION: The precise and timely detection of plant diseases plays a crucial role in ensuring efficient crop management and disease control. Nevertheless, conventional methods of disease identification, which heavily rely on manual visual inspection, are often time-consuming and susceptible to human error. The knowledge acquired from this research paper enhances the overall comprehension of the discipline and offers valuable direction for future progressions in the application of deep learning for the identification of plant diseases.[1][2] AIM: to investigate the utilization of deep learning techniques in identifying various Malvaceae plant diseases. METHODS: AlexNet, VGG, Inception, REsNet and other CNN architectures are analyzed on Malvaceae plant diseases specially on Cotton, Ocra and Hibiscus, different data collection methods ,Data augmentation and Normalization techniques. RESULTS: Inception V4 have Training Accuracy 98.58%, VGG-16 have Training Accuracy 84.27%, ResNet-50 have Training Accuracy 98.72%, DenseNet have Training Accuracy 98.87%, Inception V4 have Training Loss 0.01%, VGG-16 have Training Loss 0.52%, ResNet-50 have Training Loss 6.12%, DenseNet have Training Loss 0.016%, Inception V4 have Test Accuracy 97.59%, VGG-16 have Test accuracy 82.75%, ResNet-50 have Test Accuracy 98.73%, DenseNet have Test Accuracy 99.81%, Inception V4 have Test Loss 0.0586%, VGG-16 have Test Loss 0.64%, ResNet-50 have Test Loss 0.027%, DenseNet have Test Loss 0.0154% . CONCLUSION: conclusion summarizes the key findings and highlights the potential of deep learning as a valuable tool for accurate and efficient identification of Malvaceae plant diseases.

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Leaf diseases detection empowered with transfer learning model

<p>The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.<strong></strong></p>

Early detection of tomato leaf diseases based on deep learning techniques

<span lang="EN-US">Tomato leaf diseases are a big issue for producers, and finding a single method to combat them is tough. Deep learning techniques, notably convolutional neural networks (CNNs), show promise in recognizing early indicators of illness, which can help producers avoid costly concerns in the future. In this study, we present a CNN-based model for the early identification of tomato leaf diseases to preserve output and boost yield. We used a dataset from the plantvillage database with 11,000 photos from 10 distinct disease categories to train our model. Our CNN was trained on this dataset, and the suggested model obtained an astounding 96% accuracy rate. This shows that our method has the potential to be efficient in detecting tomato leaf diseases early on, therefore assisting producers in managing and reducing disease outbreaks and, as a result, resulting in higher crop yields.</span>

Recognition of mulberry leaf diseases based on multi-scale residual network fusion SENet.

Silkworms are insects with important economic value, and mulberry leaves are the food of silkworms. The quality and quantity of mulberry leaves have a direct impact on cocooning. Mulberry leaves are often infected with various diseases during the growth process. Because of the subjectivity and time-consuming problems in artificial identification of mulberry leaf diseases. In this work, a multi-scale residual network fusion Squeeze-and-Excitation Networks (SENet) is proposed for mulberry leaf disease recognition. The mulberry leaf disease dataset was expanded by performing operations such as brightness enhancement, contrast enhancement, level flipping and adding Gaussian noise. Multi-scale convolution was used instead of the traditional single-scale convolution, allowing the network to be widened to obtain more feature information and avoiding the overfitting phenomenon caused by the network piling up too deep. SENet was introduced into the residual network to enhance the extraction of key feature information of the model, thus improving the recognition accuracy of the model. The experimental results showed that the method proposed in this paper can effectively improve the recognition performance of the model. The recognition accuracy reached 98.72%. The recall and F1 score were 98.73% and 98.72% respectively. Compared with some other models, this model has better recognition effect and can provide technical reference for intelligent mulberry leaf disease detection.

Enhancing plant leaf disease detection: Integrating krill herd optimization-surf features and deep belief network

A plant disease that attacks the leaf causes significant yield and market value losses. A professional plant pathologist should be able to visually identify the disease by looking at the affected plant leaves, but this is unlikely to result in a more accurate diagnosis. Disease symptoms should be immediately recognisable in order to stop the spread of the illness. To find plant diseases, steps should be taken using computer assisted technologies. Numerous methods for identifying plant diseases using machine learning (ML) and deep learning (DL) have been developed and tested in numerous studies. Machine learning has the disadvantages of having a small dataset, taking longer, and requiring more time for results interpretation. Deep learning is suggested as a solution to this. This study compares the effectiveness of both ML&DL for plant leaf disease identification with more recent investigations. The common deep learning technique involves utilising the Krill Herd Optimisation Algorithm (KHO) to segment images and the Speeded up Robust Features (SURF) to extract the images. The Artificial Bee Colony (ABC) then chooses the features. Then, a Deep Belief Network (DBN) can be used to classify the chosen image. Multiple diseases can be identified on the same leaf using this method. This study demonstrates that deep learning outperforms machine learning in terms of results. The outcome demonstrates that the deep learning method is superior for the diagnosis of plant disease when there is sufficient data available. Using this technique, the validity and consistency were also examined.

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

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.

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.

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.

Lightweight Network for Corn Leaf Disease Identification Based on Improved YOLO v8s

This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO v8s backbone. This adaptation involves swapping the head’s C2f (Coarse-to-Fine) and Conv (Convolutional) modules with C3 Ghost and GhostNet, simplifying the model architecture while significantly amplifying detection speed. Additionally, a lightweight attention mechanism, Triplet Attention, is incorporated to refine the accuracy in identifying the post-neck layer output and to precisely define features within disease-affected areas. By introducing the ECIoU_Loss (EfficiCLoss Loss) function, replacing the original CIoU_Loss, the algorithm effectively mitigates issues associated with aspect ratio penalties, resulting in marked improvements in recognition and convergence rates. The experimental outcomes display promising metrics with a precision rate of 87.50%, a recall rate of 87.70%, and an mAP@0.5 of 91.40% all within a compact model size of 11.20 MB. In comparison to YOLO v8s, this approach achieves a 0.3% increase in mean average precision (mAP), reduces the model size by 50.2%, and significantly decreases FLOPs by 43.1%, ensuring swift and accurate maize disease identification while optimizing memory usage. Furthermore, the practical deployment of the trained model on a WeChat developer mini-program underscores its practical utility, enabling real-time disease detection in maize fields to aid in timely agricultural decision-making and disease prevention strategies.

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