Identification Of Plant Leaf Diseases Research Articles

Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.

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

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

Plant leaf disease recognition based on improved SinGAN and improved ResNet34.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

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

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

Detection and identification of plant leaf diseases using YOLOv4.

Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers' dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study's results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

Ensemble Technique of Deep Learning Model for Identifying Tomato Leaf Diseases Based on Choquet Fuzzy Integral

As tomato leaves are often attacked by various microorganisms, pests and bacterial diseases, the yield of tomato is seriously reduced. Accurate and timely identification of tomato leaf diseases is of great significance to reduce farmers’ economic losses. Ensemble learning as a combinatorial optimization method, which can improve the generalization ability and model stability, is widely used in the field of plant leaf disease identification. However, commonly used ensemble methods such as majority voting, weighted averaging, etc. do not consider the interaction between inputs when aggregating the inputs of multiple models, such that they do not produce representative outputs. To solve this problem, this paper adds fuzzy algorithms to the ensemble method, i.e., five pre-trained deep learning models, namely VGG16, VGG19, Xception and InceptionV3 and InceptionResnet V2, are integrated using Choquet integral fuzzy integrals for four classes of tomato disease identification and classification. The experimental results show that the proposed method achieves encouraging results, with the best single deep learning model achieving 98.63% accuracy on the PlantVillage dataset and the proposed fuzzy ensemble method achieving 99.80% accuracy. For the identification of tomato diseases in natural scenarios, the proposed method achieves 97% accuracy. The method can effectively identify tomato diseases.

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.

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

Intelligent serial cascade of hybrid deep learning model for plant leaf disease identification and classification with multi-scale dilation assisted 3D-CNN features

Intelligent serial cascade of hybrid deep learning model for plant leaf disease identification and classification with multi-scale dilation assisted 3D-CNN features

Utilizing Multi-Class Support Vector Machine to Detect Plant Leaf Diseases

Abstract: Plant disease has a large influence on agricultural output and food security. For successful illness management, early detection and correct diagnosis of these disorders are critical. Digital image processing techniques have emerged as a viable tool for automated plant leaf disease identification in recent years. The goal of this study is to use image processing to create a more efficient and accurate system for detecting leaf illness. K-means clustering, Gray-Level Co-Occurrence Matrix (GLCM), and Support Vector Machine are used to create the model. Kmeans clustering is employed for image segmentation, GLCM, and multiclass SVM for feature extraction and illness classification respectively. The maximum accuracy is 98.4%

PLANT LEAF DISEASE DETECTION

In India, agriculture is the main driver of economic growth. Farmers choose the best crops for each season based on soil fertility, weather, and crop economics. Agricultural industries strive for better ways to produce food in order to meet the demands of an expanding population. New technologies that would increase yields while lowering investment are being sought after by researchers. A new technology called precision aids in enhancing farming practices. This makes it one of the most significant and vital factors to take into account when looking for plant illnesses. The notable uses of precision agriculture include the identification of plant leaf diseases. The main goal of this project is to distinguish between healthy and unhealthy leaf plants by utilizing the CNN algorithm to extract features from input photos.

A Review of Image Processing and Machine Learning for Plant Leaf Disease Identification

A Review of Image Processing and Machine Learning for Plant Leaf Disease Identification

CLA: A self-supervised contrastive learning method for leaf disease identification with domain adaptation

Plant leaf diseases cause a decrease in crop yield and degrade the quality, which presents the urgent need for leaf disease identification. Recently, deep learning technologies, especially computer vision, have emerged as a powerful tool in plant leaf disease identification. However, existing methods invariably rely on large-scale labeled data for model training. Although self-supervised learning which uses large-scale unlabeled data for pre-training provides a useful scheme, these unlabeled data are messy, e.g., images with different shooting angles and backgrounds. In this regard, such data results in distribution-shifted training datasets, which degrades model performance. To address these problems, we propose a self-supervised Contrastive learning method for Leaf disease identification with domain Adaptation (CLA), including pre-training with forwarding and fine-tuning with domain adaptation stage. Specifically, CLA utilises large-scale yet messy unlabeled data to train the encoder and obtains their visual representations in the pre-training stage. Based on small labeled data, CLA trains the domain adaptation layer (DAL) and the classifier in the fine-tuning stage. Due to the DAL, CLA can align labeled data and unlabeled data in the fine-tuning stage and generates more general visual representations, which improves the domain adaptation ability of CLA. Experiments are conducted to evaluate the performance of CLA and demonstrate that CLA outperforms the comparison methods by a large margin on both domain adaptation and accuracy performance, with the highest accuracy of 90.52%. To better understand our proposed method, additional experiments are also conducted to explore the influencing factors of CLA.

DFNet: Dense fusion convolution neural network for plant leaf disease classification

AbstractThe early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single‐CNN model, DFNet relies on a double‐pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases.

A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification

This study proposed a novel framework for plant leaf disease identification. The proposed model consists of four steps including pre-processing, segmentation, feature extraction, and classification. At first, the unwanted noise and overfitting are removed, and also image contrast level is enhanced. Secondly, the Fuzzy C-Means (FCM) based Chameleon Swarm Algorithm (CSA) named as (FCM-CSA) is used for plant leaf diseased part segmentation. In the third stage, the feature extraction is performed using a fast GLCM feature extraction model. Finally, the Progressive Neural Architecture Search (PNAS) is used for plant leaf disease identification. The experimental investigations are carried out using MATLAB software with the Mendeley database. From this dataset, we have used Apple Cedar Apple Rust (ACAR), Cherry Powdery Mildew (CPM), Corn Common Rust (CCCR), Apple Healthy (AH), Grape Black Rot (GBR), Pepper Bell Bacterial Spot (PBBS), Potato Late Blight (PLB) and Tomato Leaf Mold (TLM) disease images. Different measures such as precision, recall, sensitivity, specificity, and accuracy results are used to validate the performance of the proposed model.

DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection

Plant-leaf disease detection is one of the key problems of smart agriculture which has a significant impact on the global economy. To mitigate this, intelligent agricultural solutions are evolving that aid farmer to take preventive measures for improving crop production. With the advancement of deep learning, many convolutional neural network models have blazed their way to the identification of plant-leaf diseases. However, these models are limited to the detection of specific crops only. Therefore, this paper presents a new deeper lightweight convolutional neural network architecture (DLMC-Net) to perform plant leaf disease detection across multiple crops for real-time agricultural applications. In the proposed model, a sequence of collective blocks is introduced along with the passage layer to extract deep features. These benefits in feature propagation and feature reuse, which results in handling the vanishing gradient problem. Moreover, point-wise and separable convolution blocks are employed to reduce the number of trainable parameters. The efficacy of the proposed DLMC-Net model is validated across four publicly available datasets, namely citrus, cucumber, grapes, and tomato. Experimental results of the proposed model are compared against seven state-of-the-art models on eight parameters, namely accuracy, error, precision, recall, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Experiments demonstrate that the proposed model has surpassed all the considered models, even under complex background conditions, with an accuracy of 93.56%, 92.34%, 99.50%, and 96.56% on citrus, cucumber, grapes, and tomato, respectively. Moreover, the proposed DLMC-Net requires only 6.4 million trainable parameters, which is the second best among the compared models. Therefore, it can be asserted that the proposed model is a viable alternative to perform plant leaf disease detection across multiple crops.

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

SummaryRice is the major crop in India. Early prevention and timely identification of plant leaf diseases are important for increasing production. Hence, an effective sunflower earthworm algorithm and student psychology based optimization (SEWA‐SPBO) based deep maxout network is developed to classify different types of diseases in rice plant leaf. The SEWA is the combination of sunflower optimization (SFO) and earthworm algorithm (EWA). Initially, the network nodes simulated in the environment capture the plant leaf images and are routed to the sink node for disease classification. After receiving the plant images at the sink node, the image is preprocessed using a Gaussian filter. Next to preprocessing, segmentation using the black hole entropic fuzzy clustering (BHEFC) mechanism is performed. Then, data augmentation is applied to segmented image results and disease classification is done by a deep maxout network. The training of the deep maxout network is done using the proposed SEWA‐SPBO algorithm. The proposed method detects the leaf disease more accurately with limited time and shows higher accuracy. Moreover, the proposed method attains higher performance with metrics, like accuracy, sensitivity, and specificity as 93.626%, 94.626%, and 90.431%, respectively.

Automatic Image Segmentation for the Detection of Illness in Cash Crops Extended Data Set Method & Deep Learning

The economy of our country heavily depends on agrarian produce. This is the driving force behind Recognizing unhealthy leaves is the key to preventing crops from declining and yield from declining. It required a tremendous amount of labour, knowledge of the leaf diseases, and a tremendous lot of time. As a result, methods for image processing are used to find and identify unhealthy plant leaf conditions. Automatically identifying plant leaf diseases is helpful because it reduces the laborious task of observing in large farms and identifies disease symptoms right away. The stages of image acquisition, image pre-processing, picture segmentation, feature extraction, and classification are involved in the detection and identification of plant leaf diseases. The methodologies for pre-processing images, picture segmentation algorithms for automatic recognition, and research on potential plant leaf disease classification algorithms are all included in this work.

MULTINET: A Multi-Agent DRL and EfficientNet Assisted Framework for 3D Plant Leaf Disease Identification and Severity Quantification

MULTINET: A Multi-Agent DRL and EfficientNet Assisted Framework for 3D Plant Leaf Disease Identification and Severity Quantification

Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification

This research examines and explores four different pre-trained CNN deep learning models (AlexNet, VGG16, ResNet50, and DenseNet121) to be adopted as edge solution. The model is developed and evaluated using the PlantVillage dataset. Image transformation techniques and down sampling were carried out to mitigate the unbalanced class distribution problem. From the preliminary works, DenseNet121 was selected as the implementation model since it had the best accuracy (96.4%) in comparison with other models. The model is then tested on different endpoint devices (CPU, GPU and VPU) in different programming environments (standard PyTorch and OpenVINO) to test the consistency of models that work across different hardware and software configurations. Results show that the adoption of model on the edge (VPU) is able to maintain relatively high recall values, precision and F1 scores. The successful application of the model on VPU device shows the potential of the model to be implemented for the detection of plant diseases as edge solution/embedded application. • Plant disease identification deep learning model has been developed. • Model performance in different hardware and environment settings has been compared. • The model in edge setting with VPU has a better accuracy and inference time.