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In Plectropomus leopardus , Vibrio disease and Hirudo parasitic disease are relatively common. Timely recognition of these diseases can improve the survival rate of Plectropomus leopardus and prevent their spread. However, early-stage diseases are difficult to distinguish due to their small size and subtle characteristics. Traditional manual recognition methods rely on personal experience and subjective judgment, leading to time-consuming and error-prone diagnoses. To address the challenges in detecting and classifying Plectropomus leopardus diseases, this paper proposes PLDNet (Plectropomus Leopardus Disease Detection Network), a real-time detection and recognition method that provides faster and more accurate diagnoses for fish farms. PLDNet incorporates two significant advancements: First, it employs FocalModulation, which enhances the model's ability to identify key disease characteristics in images. Second, it introduces the MPDIoU (Minimum Point Distance-based Intersection over Union) for bounding box similarity comparison, optimizing the loss function and improving recognition accuracy. This paper also presents the PLDD (Plectropomus Leopardus Disease Dataset), a newly developed dataset that includes comprehensive images of healthy and diseased specimens. PLDD addresses the scarcity of data for this species and serves as a valuable resource for advancing research in marine fish health. Empirical validation of PLDNet was conducted using the PLDD dataset and benchmarked against leading models, including YOLOv8-n, YOLOv9-m, and YOLOv9-c. The results show that PLDNet achieves superior detection performance, with an average detection accuracy of 84.5%, a recall rate of 86.6%, an mAP@o.5 of 88.1%, and a real-time inference speed of 45 FPS. These metrics demonstrate that PLDNet significantly outperforms other models in both accuracy and efficiency, providing practical solutions for real-time fish disease management. [ABSTRACT FROM AUTHOR]

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of detecting inferior fruits in table grapes in natural environments, focusing on subtle appearance differences, occlusions, and fruit overlaps. We propose an enhanced green grape fruit disease detection model named RF-YOLOv7. The model is trained on a dataset comprising images of small fruits, sunburn, excess grapes, fruit fractures, and poor-quality grape bunches. RF-YOLOv7 builds upon the YOLOv7 architecture by integrating four Contextual Transformer (CoT) modules to improve target-detection accuracy, employing the Wise-IoU (WIoU) loss function to enhance generalization and overall performance, and introducing the Bi-Former attention mechanism for dynamic query awareness sparsity. The experimental results demonstrate that RF-YOLOv7 achieves a detection accuracy of 83.5%, recall rate of 76.4%, mean average precision (mAP) of 80.1%, and detection speed of 58.8 ms. Compared to the original YOLOv7, RF-YOLOv7 exhibits a 3.5% increase in mAP, with only an 8.3 ms increase in detection time. This study lays a solid foundation for the development of automatic detection equipment for intelligent grape pruning. [ABSTRACT FROM AUTHOR]

Simple Summary: Extracellular Vesicles (EVs) hold immense potential as non-invasive biomarkers and drug-delivery vehicles for infectious diseases. Secreted by most human cells, EVs carry specific DNA, RNA, proteins, and metabolites, with their contents reflecting the individual's clinical condition and cellular state. This makes them valuable tools for understanding disease mechanisms. EVs are being extensively explored as biomarkers in complex diseases, including various infections. There is a connection between EVs and pathogen-host interactions. However, significant gaps remain in understanding the roles of EVs in infection, pathogenesis, and related immune mechanisms. Addressing these gaps is crucial for advancing diagnostic and therapeutic strategies. In this article, we have examined how EVs can serve as diagnostic biomarkers in infectious diseases. These advances pave the way for EVs as biomarkers, highlighting the importance of EVs in the future of diagnostics and precision medicine. Extracellular vesicles (EVs) are nanosized vesicles that are secreted by all cells into the extracellular space. EVs are involved in cell-to-cell communication and can be found in different bodily fluids (bronchoalveolar lavage fluid, sputum, and urine), tissues, and in circulation; the composition of EVs reflects the physiological condition of the releasing cell. The ability to use EVs from bodily fluids for minimally invasive detection to monitor diseases makes them an attractive target. EVs carry a snapshot of the releasing cell's internal state, and they can serve as powerful biomarkers for diagnosing diseases. EVs also play a role in the body's immune and pathogen detection responses. Pathogens, such as bacteria and viruses, can exploit EVs to enhance their survival and spread and to evade detection by the immune system. Changes in the number or contents of EVs can signal the presence of an infection, offering a potential avenue for developing new diagnostic methods for infectious diseases. Ongoing research in this area aims to address current challenges and the potential of EVs as biomarkers in diagnosing a range of diseases, including infections and infectious diseases. There is limited literature on the development of EVs as diagnostic biomarkers for infectious diseases using existing molecular biology approaches. We aim to address this gap by reviewing recent EV-related investigations in infectious disease studies. [ABSTRACT FROM AUTHOR]

The global attention to the utilization of unmanned aerial vehicle remote sensing drones in crop disease-wide detection has led to the urgent need to find an adapted model for different environmental conditions. Therefore, the current study has focused on spatiotemporal usage of different multispectral cameras in acquiring spectral reflectance models of in-field rice bacterial blight stresses. Where, long short-term memory (LSTM) model was compared with the other models in transfer learning strategy for assessing the blight stress severity. The results revealed that by extracting 30% of the data from the target domain and transferring it to the source domain, the adaptability of the model across different sites was effectively enhanced. Besides, LSTM showed high tuning transfer efficiency that demonstrated optimal predictive performance and the shortest training time in transfer tasks. Its coefficient of the prediction set was 0.82, and its residual prediction deviation has reached 2.26. In practice, LSTM enabled the acquisition of reliable prediction results at a minimal sample collection cost while circumventing feature reduction resulting from inter-domain data alignment. When the transfer ratio reached 20%, the coefficient of determination of the prediction set reached 0.71, and the residual prediction deviation reached 1.79. The novelty of this study came from the transfer learning efficiency in improving the model’s application capabilities across the different sites, environment, and unmanned aerial vehicle in farmland disease detection. [ABSTRACT FROM AUTHOR]

In today's world, people are more prone to diseases due to food adulteration and pollution in the environment, and people have found a way of using herbal medicine as an alternative to allopathic medicine, especially since coronavirus disease 2019 (COVID-19). Medicinal plants are the source of herbal medicines that increase the immunity of humans. Medicinal plants are used in many applications, like pharmaceuticals, cosmetics, and drugs. Medicinal plants are of great importance, and hence this work presents a review of the medicinal plants grown in Karnataka State, India. The work also highlights species identification and disease detection of medicinal plants employing machine learning and deep learning approaches. The paper provides information about datasets available for various medicinal plant leaf images. The deep learning models used for species identification and disease detection in medicinal plants have been discussed along with the results. [ABSTRACT FROM AUTHOR]

The demand for sensitive, rapid, and affordable diagnostic techniques has surged, particularly following the COVID-19 pandemic, driving the development of CRISPR-based diagnostic tools that utilize Cas effector proteins (such as Cas9, Cas12, and Cas13) as viable alternatives to traditional nucleic acid-based detection methods. These CRISPR systems, often integrated with biosensing and amplification technologies, provide precise, rapid, and portable diagnostics, making on-site testing without the need for extensive infrastructure feasible, especially in underserved or rural areas. In contrast, traditional diagnostic methods, while still essential, are often limited by the need for costly equipment and skilled operators, restricting their accessibility. As a result, developing accessible, user-friendly solutions for at-home, field, and laboratory diagnostics has become a key focus in CRISPR diagnostic innovations. This review examines the current state of CRISPR-based diagnostics and their potential applications across a wide range of diseases, including cancers (e.g., colorectal and breast cancer), genetic disorders (e.g., sickle cell disease), and infectious diseases (e.g., tuberculosis, malaria, Zika virus, and human papillomavirus). Additionally, the integration of machine learning (ML) and artificial intelligence (AI) to enhance the accuracy, scalability, and efficiency of CRISPR diagnostics is discussed, alongside the challenges of incorporating CRISPR technologies into point-of-care settings. The review also explores the potential for these cutting-edge tools to revolutionize disease diagnosis and personalized treatment in the future, while identifying the challenges and future directions necessary to address existing gaps in CRISPR-based diagnostic research. [ABSTRACT FROM AUTHOR]

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01. [ABSTRACT FROM AUTHOR]

The emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation metrics exceeding 80%, with the RF model (precision = 88.31%) demonstrating superior performance. Additionally, the RF model was integrated into an interface designed for agricultural decision-making. The practical application of the developed model could significantly improve the ability to detect and manage diseases in rice crops. [ABSTRACT FROM AUTHOR]

A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation. [ABSTRACT FROM AUTHOR]

Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. [ABSTRACT FROM AUTHOR]

One of the vital characteristics that determine the health condition of a person is the shape and number of the red blood cells, white blood cells and platelets present in one's blood. Any abnormality in these characteristics is an indication of the person suffering from diseases like anaemia, leukaemia or thrombocytosis. The counting of the blood cell is conventionally made by means of microscopic studies with the application of suitable chemical substances in the blood. The conventional methods pose challenges in the analysis in terms of manual labour and are time-consuming and costly tasks requiring highly skilled medical professionals. This paper proposes a novel scheme to analyse the blood sample of an individual by employing a visual question answering (VQA) system, which accepts a blood smear image as input and answers questions pertaining to the sample, viz. amount of blood cells, nature of abnormalities, etc. very quickly without requiring the service of a skilled medical professional. In VQA, the computer generates textual answers to questions about an input image. Solving this difficult problem requires visual understanding, question comprehension and deductive reasoning. The proposed approach exploits a convolutional neural network for question categorisation and an object detector with an attention mechanism for visual comprehension. The experiment has been conducted with two types of attention: (1) convolutional block attention module and (2) squeeze-and-excitation network which facilitates very fast and reliable results. A VQA dataset has been created for this study due to the unavailability of a public dataset, and the proposed system exhibited an accuracy of 94% for numeric response questions/yes or no type questions and has a BLEU score of 0.91. It is also observed that the attention-based object recognition model of the proposed system for counting the blood characteristics has an accuracy of 97%, 100% and 98% for red blood cell count, white blood cell count and platelet count, respectively, which is an improvement of 1%, 0.06% and 1.61% as compared to the state-of-the-art model. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) assisted smart farming techniques are gradually being used efficiently for identification and classification of vegetable plant diseases. Detection and classification of diseases in these plant families like Solanaceae are still problematic using DCNN due to variations in environmental conditions, genome variation, type of disease, etc. In this paper, two methods for spotting and diagnosing diseases of brinjal and tomato plants leaves named as Optimal Environmental Traversing Alert (OETA) and Optimum diagnosis of Solanaceae leaf diseases (ODSLD) respectively have been proposed. The OETA machine learning (ML) based method is used first to detect the disease, and then the ODSLD deep convolutional neural networks (DCNN) method is used to classify it. An analysis of the proposed method experiments showed that OETA disease detection for brinjal plant (eggplants) was 97.81 percent and for tomato plants was 99.03 percent. For disease classification by ODSLD method, the VGG-16 for brinjal plant and ResNet-50 for tomato plants outperformed other existing DCNN computer vision methods. [ABSTRACT FROM AUTHOR]

Abstract Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments.

This study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0.715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana.

The lack of specialized tools to check the condition of ornamental fish has hindered effective management. This research proposes a novel software architecture that uses the YOLOv9 model combined with RT-DETR to enable accurate and timely identification of ornamental fish conditions including fish diseases, empowering farmers and hobbyists with a valuable resource. This integration is done using Soft Voting Ensemble Learning technique. To achieve this goal, an Android mobile application successfully classified healthy fish and accurately identified common diseases such as bacteria, fungal, parasitic, and whitetail. Based on the test results, the integration accuracy of the YOLOv9 and RT-DETR models produced a high result of 0.8947 while the stand-alone YOLOv9 showed 0.8889 and the stand-alone RT-DETR of 0.8904. Recommendations are given for the combination of YOLOv9 and RT-DETR in condition detection and diagnosis of ornamental fish diseases.

The community service aims to introduce the application of biosafety and biosecurity and provide the ability to recognize livestock diseases simply with the participatory rural appraisal (PRA) method in reducing the transmission of livestock diseases in East Lombok Regency. The implementation methods of the activities include a survey of the service location to identify problems, socialization of the program activity to the target community, education about biosecurity and biosafety, and an introduction to simple livestock disease detection based on community participation with PRA, then continued with training on applying simple biosecurity and biosafety and simple livestock disease detection methods with PRA proportional piling and matrix scoring methods. The data of PRA proportional piling and matrix scoring based on disease symptom data will be mapped and ranked using descriptive analysis. The results of this community service were obtained from members of The Pade Angen II Livestock Group could use personal protective equipment (PPE) correctly by 80%. The detection of disease based on symptoms using the proportional piling and matrix scoring methods obtained repeat breeding events with symptoms of repeated mating of 21%, helminthiasis with symptoms of worms of 14%, itching of 4%, diarrhea of 4%, and scabies with symptoms of itching of 8%. Foot and mouth disease was also still found with symptoms of wounds on the feet and salivation of 11%, miscarriage of 4% and fever of 1%, coccidiosis with symptoms of bloody diarrhea of 10%, colibacillosis with symptoms of diarrhea of 6%, bloat with symptoms of bloating of 3% and fever of 1%.

Detecting diseases in apple leaves accurately and efficiently is vital for maintaining healthy crops and ensuring optimal yield. This paper introduces a novel approach that integrates Neutrosophic Logic with the EfficientNetB0 model to enhance the classification of apple leaf diseases. The proposed method significantly improves precision, recall, and F1-scores across multiple disease classes, demonstrating its robustness and effectiveness compared to traditional techniques. [ABSTRACT FROM AUTHOR]

Biosecurity plans are crucial for preventing economic and environmental impacts caused by disease outbreaks in aquaculture. These plans focus on prevention, early detection, and effective control measures. With the global threat of emerging infectious diseases and the need for sustainable production practices, the importance of biosecurity continues to grow. Scientometric analysis is a valuable tool for assessing the impact and influence of scientific research within a particular field or discipline. Scientometric analysis of aquaculture biosecurity publications reveals significant activity in the field, with the highest number of publications recorded in 2021. Cottier-Cook EJ emerged as the most prolific author, while USA and the Centre for Environment Fisheries Aquaculture Science were identified as leading contributors. Physical biosecurity measures prevent the entry of pathogens and wild fish into aquaculture systems. Biological biosecurity measures enhance immunity and reduce disease risks. Operational biosecurity measures, such as feed management and hygiene protocols, maintain animal health. Innovative technologies such as sensors and artificial intelligence improve biosecurity efficiency. Effective management of disease outbreaks requires coordination, risk assessment, and established response plans. Aquaculture biosecurity offers benefits such as disease prevention, environmental protection, and food safety, but may have disadvantages including costs and negative environmental impacts. The industry should focus on implementing effective and sustainable biosecurity measures, improving disease prevention, reducing environmental impact, and ensuring product safety and quality. [ABSTRACT FROM AUTHOR]

Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments. [ABSTRACT FROM AUTHOR]

This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research. [ABSTRACT FROM AUTHOR]

A comprehensive examination of the application of pre-trained Convolutional Neural Networks (CNNs) was discussed, such as GoogLeNet, VGGNet and EfficientNet in detecting apple leaf diseases and pests. By addressing the limitations and gaps in existing research, we focused on enhancing detection accuracy by leveraging deep features extracted from these CNN models. The methodology involved the fusion of deep features obtained from the final fully connected layers of the CNNs, followed by the training of a Support Vector Machine (SVM) classifier. Results showed that all the CNN models demonstrated significant accuracy in detecting apple leaf diseases using deep feature extraction, achieving an overall classification accuracy of 99. 42%. Furthermore, an improved deep learning approach was introduced which combined the deep features from the three CNN models, further boosting predictive performance. The methodology exhibited promising results in apple leaf disease detection and had potential applications in detecting diseases in other plant leaves. This research contributed to the development of automated and precise plant disease identification techniques, paving the way for intelligent and targeted agricultural production. [ABSTRACT FROM AUTHOR]

Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80% training data and 20% test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface (GUI). The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3%, ResNet50 90.95%, VGG-19 93.32%, VGG-16 92.89%, ShuffleNet 89.44%, GoogLeNet 87.5%, MobileNet-v2 87.5%, EfficientNet-b0 85.56%, and DarkNet53 91.59%. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry. [ABSTRACT FROM AUTHOR]

Background: Anemia is a significant global health concern, often stemming from iron deficiency or deficiencies in folate, vitamins B12, and A. Anemia disproportionately impacts vulnerable populations like children, adolescent girls, and pregnant or postpartum women. Purpose: Anemia is a serious public health issue, impairing productivity, cognitive development, and increasing mortality rates. Anemia is usually detected through blood tests measuring hemoglobin levels, but non-invasive solutions are rquired to lower discomfort, enhance accessibility, and allow for regular monitoring. These methods are essential for early detection in vulnerable populations. Methodology: The research methodology involves extracting valuable information from nail images using data mining algorithms. The focus is on calculating the percentage of blue- and red-stained cells within specific regions of interest in the nail images. Machine-learning algorithms are employed to transform these data into actionable insights for disease diagnosis. Results: The system demonstrates effectiveness in accurately detecting anemia and providing prediagnosis reports to healthcare providers. The reports include comprehensive information such as patient symptoms, health history, test results, and the doctor's preliminary assessment. This aids in timely and accurate treatment decisions. Conclusion: This research showcases the potential of image processing and machine learning in improving anemia diagnosis and facilitating personalized healthcare interventions. [ABSTRACT FROM AUTHOR]

Objectives Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management. Methods We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias. Results Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares. Conclusion NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice. [ABSTRACT FROM AUTHOR]

Human diseases are categorized into infectious and non-infectious diseases. Infectious diseases are caused by pathogens such as viruses, bacteria, fungi, and parasites, while non-infectious diseases include genetic disorders, cancers, and cardiovascular diseases. Early diagnosis is crucial to manage these diseases effectively. Traditional diagnostic methods, such as PCR, face several challenges in point-of-care testing. These include the need for complex thermal cyclers for amplification, which may be cumbersome and not portable. In addition, PCR assays often require specific reagents, skilled technicians, and a controlled laboratory environment to prevent contamination and ensure accurate results. The setup time and cost associated with these methods can also be prohibitive for settings with limited resources, such as remote or rural clinics. The advent of CRISPR-based diagnostics offers a promising solution to these limitations due to their simplicity, cost-effectiveness, and rapid detection capabilities. Unlike PCR, CRISPR diagnostics do not necessarily require complex thermal cycling and can often be performed at room temperature, which makes them highly adaptable for resource-limited settings. Additionally, CRISPR-based methods leverage the precision of the Cas proteins to target specific genetic sequences, ensuring high sensitivity and specificity. Technologies like SHERLOCK and DETECTR enable accurate detection of nucleic acids with minimal sample preparation, facilitating faster, easier, and more accessible testing. This review examines the advancements in CRISPR-based diagnostic tools, focusing on the CRISPR/Cas system's applications in detecting infectious and non-infectious diseases, including bacterial, viral, fungal infections, cancers, and genetic disorders. The study analyzes CRISPR/Cas systems, specifically Cas9, Cas12, and Cas13, and their role in molecular diagnostics. Various detection methods using these systems are discussed, including CRISPR-based mutation profiling, point-of-care applications, and the detection of single nucleotide polymorphisms (SNPs). Furthermore, the review explores the challenges of multiplexed and preamplification-free disease tests. CRISPR-based diagnostics offer increased sensitivity, specificity, and speed over traditional methods, enabling precise detection of nucleic acids and protein biomarkers. The ability to perform in resource-limited settings makes CRISPRbased diagnostics an ideal solution for point-of-care testing. CRISPR/Cas-based diagnostics represent a transformative technology in precision pathology. The system's versatility and efficiency make it a valuable tool for detecting a wide range of diseases, improving early diagnosis, and enabling rapid, on-site testing. Further advancements in CRISPR-based diagnostics will enhance healthcare accessibility and outcomes. [ABSTRACT FROM AUTHOR]

Refractive errors are defined as an impairment in the eye's capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients. [ABSTRACT FROM AUTHOR]

Agricultural production faces many challenges, such as disease and pest infestation, which can lead to severe crop loss and environmental impacts due to the excessive use of chemicals. Artificial intelligence has become a key technique to solve different agricultural-related challenges. The main objective of this study was to train and validate artificial intelligence algorithms for the detection of Banana Bunchy Top Virus (BBTV) in banana crops. Approximately 2,500 images of healthy and BBTV-infected leaves were collected, stratified according to the stage of plant development, and used to calibrate and validate an artificial intelligence algorithm for the detection of BBTV. Pre-trained models such as VGG 16, ResNet50, and InceptionV3 were tested. The ResNet50 model achieved a training accuracy of 99.56% and validation precision, recall, and F1 score of 96.53%, 94.94%, and 95.73%, respectively, outperforming the other models in detecting BBTV-infected plants. [ABSTRACT FROM AUTHOR]