Recognition Of Disease Research Articles

Deep Learning-based Agricultural Pest and Disease Recognition

With the rapid development of artificial intelligence, deep learning technology has penetrated into a variety of different fields. Agricultural researchers have used deep learning's strong image and data processing capabilities to apply it to agricultural production, especially in the identification of agricultural pests and diseases, which has achieved remarkable results. The use of deep learning-based agricultural pest and disease identification technology can quickly and non-destructively identify crops in various complex environments, with high accuracy and good stability, so that farmers can quickly take effective control measures to minimize the loss of crops. This paper first explains the significance of deep learning-based agricultural pest identification technology to agricultural development, and makes a detailed analysis of the advantages and disadvantages of various pest identification technologies and their performance in pest and disease identification. Secondly, various key technologies of deep learning are introduced, including the concepts and applications of various core technologies such as data source, data preprocessing, data augmentation, network model selection, and transfer learning. Finally, based on the above research results, the problems encountered by deep learning in the identification of agricultural pests and diseases are analyzed, and its future development direction in the identification of pests and diseases is discussed.

Multi-Class Recognition of Soybean Leaf Diseases using a Conv-LSTM Model

This research presents an innovative approach for multi-class recognition of soybean leaf diseases using a Convolutional Long Short-Term Memory (Conv-LSTM) model. The model integrates the spatial learning capabilities of convolutional layers with the temporal dependencies of LSTM units, addressing the critical need for accurate disease detection in agriculture, particularly in soybean cultivation where leaf diseases significantly impact crop yield and quality. Through comparative experiments with established deep learning models such as AlexNet, VGG16, and ResNet50, the Conv-LSTM model demonstrates superior performance in terms of accuracy, precision, recall, and F1 score. By effectively capturing both spatial and temporal features in soybean leaf images, the Conv-LSTM model showcases its potential to enhance disease detection accuracy, supporting precision agriculture practices and enabling timely interventions to mitigate crop losses caused by diseases.

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.

An analysis to investigate plant disease identification based on machine learning techniques

AbstractIn agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

Subclinical Echocardiographic Parameters and Biomarker Approach to Characterize the Pathophysiology of Carcinoid Syndrome

Introduction: Carcinoid heart disease is a rare cause of right-sided valvular dysfunction, an important complication in patients with carcinoid syndrome. Objectives: We aim to present our own experience regarding early myocardial dysfunction in patients with carcinoid syndrome. Methods: 64 patients (35-90 year old, 62,5% female, 37,5% male patients) with carcinoid syndrome were evaluated between October 2017 and June 2021. All patients underwent two-dimensional echocardiography with additional tissue Doppler and speckle-tracking techniques. For all patients there were measured urinary level of 5- hydroxyindolacetic acid, serotonin and NT-proBNP values. Results: The following echocardiographic features: global longitudinal strain of both ventricles, mean systolic velocity of the left ventricle, systolic velocity of the free wall of right ventricle, correlated with high values of specific biomarkers: serotonin, and urinary 5-hydroxyindolacetic acid, and also with the presence of hepatic metastases and poorly differentiated tumors. Discussion: In this study we identified several patients with early myocardial dysfunction due to carcinoid syndrome, representing a predictive model of poor prognosis, requiring further investigations. Conclusion: Echocardiographic assessment based on the measurement of systolic velocities, as well as the global longitudinal strain of both ventricles, together with specific biomarkers may contribute to better recognition of carcinoid heart disease in clinical practice, but this needs validation through further studies enrolling larger number of patients.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

A dual-branch neural network for crop disease recognition by integrating frequency domain and spatial domain information

Crop diseases severely affect crop yield and quality, and accurate identification of crop diseases is crucial for disease management. Although deep neural networks have made progress in the task of crop disease identification, the complex environment of crop diseases, including background interference, morphological differences, and scale variations, has led to limited accuracy in disease recognition. To address these issues, this study proposes a dual-branch deep neural network for crop disease identification, integrating both frequency domain and spatial domain information. The frequency branch takes frequency domain information as input to extract rich crop disease frequency component features, while the deformable attention Transformer branch excels in representing global features and selectively focusing on local features of crop diseases. A new fusion method, Multi-Spectral Channel Attention Fusion (MSAF), is adopted to better integrate crop disease frequency and spatial features. Additionally, an improved bias loss function (cv_bias) is proposed to optimize the dual-branch network model, achieving an accuracy of 96.7 % on the test dataset, which is 2.0 % higher than the existing state-of-the-art deformable attention Transformer model. With only 14 M model parameters, this study's model provides an effective method for future applications in complex environment.

Fruit and vegetable disease detection and classification: Recent trends, challenges, and future opportunities

Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring.

Opportunities to Reduce the Risk of Cardiovascular Death by Improving Machine Learning Methods

Improving algorithms for automatic recognition of electrocardiograms requires increasing of training dataset, which is not always possible due to the rarity of certain cardiac diseases or ethical issues. It is possible to improve the algorithms for generating synthetic electrocardiograms using mechanistic models and generative-descriptive neural networks (GANs). At the same time, when evaluating the effectiveness of the proposed solutions, various authors offer different quality assessment metrics from subjective expert assessment to the squared mean error. We compare two approaches to generating synthetic electrocardiograms: pseudo-ECG generation using a one-dimensional cardiomyocyte model and GAN based on long-term short-term memory in terms of machine learning metrics: accuracy, recall, f1-score. We solve the problem of binary classification with bagging method, class 0 — normal sinus rhythm, class 1 — atrial fibrillation. We found that classifier trained on synthetic ECGs generated using GAN is slightly more effective compared to pseudo-ECGs generated using mechanistic models ones. At the same time, it turned out that GANs are not stable enough. The generation of synthetic electrocardiograms using both mechanistic models and GAN can be used to enrich the training set in case of recognition of rare cardiac diseases.

Identification of rice disease under complex background based on PSOC-DRCNet

Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image capture, as well as challenges in determining the optimal learning rate during the training of traditional rice disease recognition networks, a novel method based on PSOC-DRCNet is proposed for rice disease recognition.. First, tto solve the problem of background interference, Dual Mode Attention (DMA) is proposed to adaptively capture meaningful regions in rice disease images. Second, the Residual Adaptive Block(RAB) is proposed, which utilizes dimensional changes and channel attention to solve edge blur problems. Then, a Cross entropy and regularized mixed Loss function (CerLoss), is proposed to optimize the learning strategy of the model in the process of processing datasets and enhance the performance and generalization ability of the model to avoid overfitting problems. Ultimately, In response to the cumbersome problem of finding the optimal learning rate, we propose using Particle Swarm Optimization Chameleon (PSOC) to find the optimal learning rate and train the PSOC-DRCNet model on our custom dataset and compare it with other existing methods and the final average classification accuracy of PSOC-DRCNet is 93.88% with an F1 score of 0.940. We compare it with other existing methods. It is proved that the average classification accuracy of our model under hyper-parameter unification is 92.65% F1 score is 0.928. We validated the PSOC-DRCNet by conducting comparative analyses with other models and through generalization experiments and module effectiveness tests. Additionally, the practicality of PSOC-DRCNet was confirmed through its application in real-world scenarios. The methods proposed in this paper successfully enable the identification of various diseases in rice leaves, offering a practical solution for incorporating deep learning into the agricultural production process. Furthermore, these findings serve as a valuable reference for disease identification in other crops.

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.

Neural Greenery: Advancement in Plant Leaf Diseases Recognition Using Deep Learning

Agriculture is the backbone of our country. Large section in our country is highly dependent on agriculture. High production of crop provides global food security. High production largely depends on health of plant. Plant health is greatly affected by pest, climatic condition etc. Thus, early detection of these disease is very necessary to stop further spread of the disease. In this paper, we have proposed a real-time plant leaf disease detection system exploring deep learning techniques. This model is capable of recognizing several types of plant disease using real time data of leave. Our approach utilizes convolutional neural networks (CNNs) to automatically extract relevant features from leaf images, enabling accurate classification of healthy and diseased leaves. Firstly, all enlargement is applied on dataset to increase the sample size. Convolution Neural Network (CNN) is used with several convolution and pooling layers. Botanical community dataset is used to train the model. After training the model, it is examined properly to validate the results. Keywords: Plant Disease, Convolution Neural Network (CNN), Deep Learning, Agriculture, and Botanical community.

IOTeeth

While occlusal diseases - the main cause of tooth loss -- significantly impact patients' teeth and well-being, they are the most underdiagnosed dental diseases nowadays. Experiencing occlusal diseases could result in difficulties in eating, speaking, and chronicle headaches, ultimately impacting patients' quality of life. Although attempts have been made to develop sensing systems for teeth activity monitoring, solutions that support sufficient sensing resolution for occlusal monitoring are missing. To fill that gap, this paper presents IOTeeth, a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. The IOTeeth system includes an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform to support reliable occlusal disease recognition. IOTeeth focuses on biting and grinding activities from the canines and front teeth, which contain essential information of occlusion. IOTeeth's intra-oral wearable collects signals from the sensors and fetches them into a lightweight and robust deep learning model called Physioaware Attention Network (PAN Net) for occlusal disease recognition. We evaluate IOTeeth with 12 articulator teeth models from dental clinic patients. Evaluation results show an F1 score of 0.97 for activity recognition with leave-one-out validation and an average F1 score of 0.92 for dental disease recognition for different activities with leave-one-out validation.

Neural network based on convolution and self-attention fusion mechanism for plant leaves disease recognition

In agriculture, early and timely detection and identification of plant disease categories can help growers take timely countermeasures. The use of deep learning techniques for plant disease category detection prevents further spread of the disease and helps to prevent crop production losses. In this paper, Based on the Next-Vit neural network model, we proposed a lightweight neural network CAST-Net based on the combination of convolution and self-attention, and we adopted self-distillation based on this model to achieve increased accuracy in classifying plant leaf diseases while reducing the number of model parameters and flops. Our model and method achieved 98.4% accuracy on the tomato subset of the data-enhanced PlantVillage dataset, a 4.9% improvement over the Next-Vit model, and 99.0% accuracy on the full PlantVillage set, a 6.9% improvement over the Next-Vit model. We also propose a new dynamic learning rate function that is applied to the training phase to prevent the loss from reaching the optimal value. The results show that our model and method have higher accuracy, fewer parameters, shorter training time and lower computational complexity than existing models.

Computer Vision for Plant Disease Recognition: A Comprehensive Review

Computer Vision for Plant Disease Recognition: A Comprehensive Review

Plant disease recognition in a low data scenario using few-shot learning

Plant disease is one of the major problems in agriculture. Diseases damage plants, reduce yields and lower the quality of the produce. Traditional approaches to detecting plant diseases are usually based on visual inspection and laboratory testing, which can be expensive and time-consuming. They require trained plant pathologists as well as specialised equipment. Several studies demonstrate that artificial intelligence (AI) methods can produce promising results. However, AI methods are generally data-hungry and require large annotated datasets, and the collection and annotation of such datasets can be a limiting factor. It often appears that only a small amount of data is available for certain disease types. Whereas the performance of typical AI methods drops significantly when they are trained with inadequate data. This paper proposes a novel few-shot learning (FSL) method to detect plant diseases and alleviate the data scarcity problem. The proposed method uses as few as five images per class in the machine learning process. Our method is based on a state-of-the-art FSL pipeline called pre-training, meta-learning, and fine-tuning (PMF), integrated with a novel feature attention (FA) module; we call the overall method PMF+FA. The FA module emphasises the discriminative parts in the image and reduces the impact of complicated backgrounds and undesired objects. We used ResNet50 and Vision Transformers (ViT) as the feature learner. Two publicly available plant disease datasets were repurposed to meet the FSL requirements. We thoroughly evaluated the proposed method on the PlantDoc dataset, which contains disease samples in field environments with complex backgrounds and unwanted objects. The PMF+FA method with ViT achieved an average accuracy of 90.12% in disease recognition. The results demonstrate that the PMF+FA pipeline consistently outperforms the baseline PMF. The results also highlight that the method using ViT generates better results than ResNet50 for diagnosing complex data. ViT and ResNet50 implementations are computationally efficient, taking 1.11 and 0.57 ms on average per image to evaluate the test set respectively. The high throughput and high-quality performance with only a small training dataset indicate that the proposed technique can be used for real-time disease detection in digital farming systems.

Peripheral artery disease recognition, diagnosis, and management in general practice in the Republic of Ireland and England: an online survey.

Peripheral artery disease (PAD) is common and associated with future cardiovascular events. PAD is underdiagnosed, which limits opportunities to address secondary prevention of cardiovascular disease. It is unknown how closely guidelines for detection of PAD are followed in primary care. To survey GPs' attitudes to diagnosis and follow-up of patients with PAD. Online survey of GPs in England and the Republic of Ireland (RoI). GPs' approaches to management of PAD were assessed using likelihood ratings (scales of 0-10) and discrete questions. Findings were summarised as proportions, or median and interquartile ranges (IQR). In total, 111 responses were analysed; 68 (61%) from England and 43 (39%) from the RoI. Considering a hypothetical patient at risk of PAD, likelihood of GPs enquiring about PAD symptoms (leg pains: 3/10 or erectile dysfunction: 2/10) was low. GPs in the RoI compared with GPs in England more often examined the heart (10/10 versus 7/10) or carotid vessels (5/10 versus 1/10). Lower limb pulses were palpated in response to symptoms or signs of PAD. In England 25% of practitioners, and in the RoI 55% of practitioners, reported that they do not measure ankle-brachial index (ABI). Currently, detection of PAD is generally triggered by 'classical' leg claudication symptoms, while known vascular risk factors appear to elicit little consideration. ABI measurement is not performed by many practitioners, suggesting that a proportion of vascular referrals must be based on history and examination findings alone. Opportunities to recognise PAD are missed.

Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves

One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have revolutionized the field of plant disease detection, offering advanced and accurate solutions for early identification and management. However, a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset. In this paper, we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops, even if the system was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. To confirm the effectiveness of our method, we trained and tested it using the widely acclaimed PlantVillage dataset, recognized as the most utilized dataset for its comprehensive and diverse coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new datasets, it achieved an accuracy rate of 97.13%. This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases. In addition, it outperformed the existing methods in its ability to identify any disease across any crop type, showcasing its potential for broad applicability and contribution to global food security initiatives.

An approach to diagnosis of prostate cancer using fuzzy logic

<p><span>Early diagnosis of cancers is a major requirement for patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. The recognition of cancer diseases mostly operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system (FES) and its implementation for the detection of prostate cancer. Specifically, prostate-specific antigen (PSA), prostate volume (PV), age, and percentage free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while PCR serves as an output parameter. Mamdani fuzzy inference method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to determine whether their patients need a biopsy. This system is fast as it requires minimum calculation and hence comparatively less time which reduces mortality and morbidity and is more reliable than other economic systems and can be frequently used by doctors.</span></p>

Therapeutic itineraries of quilombola adults for oral health care in a rural district of Bahia, Brazil.

This study examined the oral health-related therapeutic itineraries of quilombola adults in a rural district of Vitória da Conquista, Bahia. This qualitative study involved ten semi-structured interviews of adult members of the quilombola community, in May 2021, which were then transcribed and analysed using content analysis. The results showed little or poor oral hygiene at some stage of life, especially in childhood and adolescence, the use of popular oral health care practices, and experiences of professional care featuring tooth extraction. Use of health services was mostly reported only in the period prior to the COVID-19 pandemic. Responses as to perceived ease of access to health services in the community varied. One common complaint as to satisfaction with oral health was the need to use or replace dental prostheses. This study concluded that oral health must be promoted jointly with disease prevention, dental rehabilitation and recognition for the knowledge and worldview of the quilombola population.