Hasil untuk "Nutritional diseases. Deficiency diseases"

Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij et al.

Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.

Balram Singh, Ram Prakash Sharma, Somnath Dey

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.

Lang Qin, Zijian Gan, Xu Cao et al.

Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in which agents dynamically construct evidence graphs from diverse perspectives to support, refute, or entail hypotheses. The reasoning strategies are analyzed post hoc in a self-evolutionary loop, producing textual feedback that refines agent policies, while successful reasoning paths are distilled into transferable heuristics to accelerate future investigations. Comprehensive evaluations reveal that RareAgent improves the indication AUPRC by 18.1% over reasoning baselines and provides a transparent reasoning chain consistent with clinical evidence.

Ziqi Li, Agnes Komey, Rachel Kopec

Aynaz velayati, Mohammadreza vafa, Mahdi Yadollahzadeh et al.

Abstract Background Acute respiratory failure (ARF) is the most common organ failure, affecting up to 30% of all intensive care unit (ICU) admissions. Since vitamin deficiencies are frequent in critically ill patients, and considering that the two primary mechanisms involved in ARF are inflammation and oxidative stress, this study aims to investigate the effects of co-supplementation of vitamins D and C on inflammatory parameters, oxidative stress, and clinical outcomes in critically ill ARF patients. Methods In this double-blind, randomized, placebo-controlled trial, ARF patients admitted to the ICU were randomly assigned to either the intervention group (daily 2000 mg of intravenous vitamin C plus 5000 IU of oral vitamin D) or the control group (placebo). The intervention lasted 10 days, and patients were followed for 90 days, assessing inflammation, oxidative stress parameters, and clinical outcomes. Results Thirty-four patients in the intervention group and 33 in the control group completed the trial. At the end of the intervention, High-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and malondialdehyde (MDA) were significantly reduced, while total antioxidant capacity (TAC) increased significantly in the intervention group compared to both the baseline and the control group (p < 0.001). Among the clinical outcomes, only the duration of ICU stay was shorter in the intervention group (0.028). However, 90-day survival (HR 0.55; 95% CI 0.21–1.41; P = 0.22) did not show a significant difference between the two groups. Conclusions Our study concluded that vitamin D and C supplementation may improve inflammation and oxidative status in critically ill ARF patients, reducing ICU stays but not affecting 90-day mortality. Trial registration The study has been registered at https://irct.behdasht.gov.ir/ . Number: IRCT20090822002365N29.Registered on 2023-08-05 and it was ethically approved by the Committee of Iran University of Medical Sciences (registration IR.IUMS.REC.1402.210).

Fanzhi Zhang, Bin Zhang, Xinfang Huang et al.

Abstract Background The triglyceride-glucose (TyG) index is an important determinant influencing the incidence of cardiometabolic multimorbidity (CMM). However, it remains unclear whether combining the TyG index with novel obesity indices (CVAI/BRI/ABSI/WWI) can improve the risk stratification of CMM. This study aimed to systematically compare the incremental risk assessment and predictive value of the TyG index, TyG-traditional obesity indices (TyG-WC/TyG-WHtR/TyG-BMI), and TyG-novel obesity indices (TyG-CVAI/TyG-BRI/TyG-ABSI/TyG-WWI) for CMM. Methods Trajectory changes and cumulative exposure of TyG-related parameters were quantified using repeated measurements from the CHARLS cohort (n = 3,885). The study endpoint CMM was defined as a comorbid condition encompassing two or more cardiometabolic diseases, namely diabetes, stroke and heart diseases. A multi-model analytical strategy was employed to evaluate the associations between TyG-related parameters and CMM, as well as the contribution of their components. The net reclassification index and integrated discrimination improvement were employed to evaluate the improvement in predictive performance of these indices. Results Over a median follow-up period of 8 years, we identified a linear positive association between TyG-related parameters and CMM, with the cumulative effects of glucose and obesity emerging as the key drivers. Compared with the baseline TyG index, the incremental risk assessment value for CMM improved by 10%-17% (baseline) and 12%-20% (cumulative exposure) for TyG-traditional obesity indices, while the improvement for TyG-novel obesity indices ranged from − 1% to 16% and 5%-19%, respectively. In summary, all TyG-traditional obesity indices demonstrated strong associations with CMM, whereas among the TyG-novel obesity indices, only TyG-CVAI showed a similarly strong association. Furthermore, all TyG-related parameters showed significantly increased hazard ratios in their highest-exposure or poor-control status versus the reference (lowest exposure or good control): TyG-index (1.69/2.05), TyG-WC (2.24/2.28), TyG-WHtR (1.92/2.05), TyG-BMI (1.85/2.27), TyG-CVAI (1.89/2.07), TyG-BRI (1.94/2.08), TyG-ABSI (1.70/1.85), and TyG-WWI (1.97/1.95). Predictive analyses showed that, except for TyG index, TyG-ABSI and TyG-WWI, all other TyG-related parameters provided a certain degree of net improvement compared with the baseline risk model. Conclusion All eight TyG-related parameters can predict the incidence of CMM. Given their relative simplicity, the TyG-traditional obesity indices demonstrate superior incremental risk assessment and predictive value for CMM compared to the TyG-novel obesity indices and the TyG index, positioning them as promising and more practical tools for clinical practice. Graphical Abstract

Hyunji Ham, Sumin Kim, Kyungho Ha

Abstract Background Sarcopenia contributes to an increased risk of falls and fractures, and reduced mobility, and mortality. Supplementation with dietary protein and amino acids has been suggested as a potential strategy to slow or prevent the associated loss of muscle mass and strength. However, most previous studies have focused on dietary protein or limited populations, such as older adults. Therefore, this study aimed to investigate the relationship between dietary protein and amino acid intake and handgrip strength (HG) in Korean adults. Methods This study used data from the 2014–2019 Korea National Health and Nutrition Examination Survey. A total of 18,565 adults who participated in a 1-day 24-hour recall method were included. Protein intake was calculated as a percentage of total energy intake from food sources (animal and plant). Amino acid intake (g/day), including essential amino acids (EAAs), branched-chain amino acids (BCAAs), and non essential amino acids (NEAAs), was assessed using a database expanded based on amino acid composition databases constructed by national institutions. Low HG was diagnosed based on the 2019 guidelines of the Asian Working Group on Sarcopenia. Results In the fully adjusted model, total and plant protein intakes were positively associated with HG levels (kg) (β = 0.04 and 0.07 per 1% increase, respectively; p < 0.05 for both). Participants aged ≥ 65 years in the highest NEAA intake group had a 42% lower risk of low HG compared to those in the lowest intake group (odds ratio 0.58; 95% confidence interval 0.35–0.97; p for trend = 0.1026). A lower risk of HG was observed in older participants whose plant protein intake ranged from 8 to 10% of energy, compared to those consuming less than 7%. However, no association was found when intake exceeded 10% of energy. Conclusions These findings suggest that a high intake of NEAAs and a moderately high intake of plant protein may be associated with a lower risk of low HG among Korean older adults. Further prospective studies are needed to explore the effects of protein and amino acid intake on muscle mass and strength.

Xihan Qin, Li Liao

Comorbidity carries significant implications for disease understanding and management. The genetic causes for comorbidity often trace back to mutations occurred either in the same gene associated with two diseases or in different genes associated with different diseases respectively but coming into connection via protein-protein interactions. Therefore, human interactome has been used in more sophisticated study of disease comorbidity. Human interactome, as a large incomplete graph, presents its own challenges to extracting useful features for comorbidity prediction. In this work, we introduce a novel approach named Biologically Supervised Graph Embedding (BSE) to allow for selecting most relevant features to enhance the prediction accuracy of comorbid disease pairs. Our investigation into BSE's impact on both centered and uncentered embedding methods showcases its consistent superiority over the state-of-the-art techniques and its adeptness in selecting dimensions enriched with vital biological insights, thereby improving prediction performance significantly, up to 50% when measured by ROC for some variations. Further analysis indicates that BSE consistently and substantially improves the ratio of disease associations to gene connectivity, affirming its potential in uncovering latent biological factors affecting comorbidity. The statistically significant enhancements across diverse metrics underscore BSE's potential to introduce novel avenues for precise disease comorbidity predictions and other potential applications. The GitHub repository containing the source code can be accessed at the following link: https://github.com/xihan-qin/Biologically-Supervised-Graph-Embedding.

Jie Tian, YangYang Fan, Xin Wei et al.

BackgroundNutritional anemia is highly prevalent and has triggered a globally recognized public health concern worldwide.ObjectiveTo better understand the prevalence of anemia and the state of nutritional health in developed countries to inform global nutritional health and better manage the disease.MethodWe employed the Healthcare Cost and Utilization Project (HCUP)-2020 National Inpatient Health Care Data (NIS), administered by The Agency for Healthcare Research and Quality. Nutritional anemia was diagnosed according to the International Classification of Diseases, 10th Revision (ICD-10). Matching analysis and multivariate regression were used to adjust for patient and hospital characteristics. Controls were obtained by stratifying and matching for age and sex.ResultsThe 2020 HCUP-NIS database encompassed a survey over 6.4 million hospitalized patients, among which 1,745,350 patients diagnosed with anemia, representing approximately 26.97% of the hospitalized population, over 310,000 were diagnosed with nutritional anemia, and 13,150 patients were hospitalized for nutritional anemia as primary diagnosis. Hospitalization rate for nutritional anemia exhibited an increased age-dependent increase nationwide, especially among females, who displayed 1.87 times higher than males. Notably, in comparison to the control group, individuals of the Black race exhibit a higher prevalence of nutritional anemia (case group: 21.7%, control group: 13.0%, p &lt; 0.001). In addition, hospitalization rates were higher among low-income populations, with lower rates of private insurance (case group: 18.7%, control group: 23.5%, p &lt; 0.001) and higher rates of Medicaid insurance (case group: 15.4%, control group: 13.9%, p &lt; 0.001). In areas characterized by larger urban centers and advanced economic conditions within the urban–rural distribution, there was an observed increase in the frequency of patient hospitalizations. Iron deficiency anemia emerged as the predominant subtype of nutritional anemia, accounting for 12,214 (92.88%). Secondary diagnosis among patients hospitalized for nutritional anemia revealed that a significant number faced concurrent major conditions like hypertension and renal failure.ConclusionIn economically prosperous areas, greater attention should be given to the health of low-income individuals and the older adult. Our findings hold valuable insights for shaping targeted public health policies to effectively address the prevalence and consequences of nutritional anemia based on a overall population health.

Yang Liu, Junjie Yao, Xiaona Xue et al.

Abstract Objective Insulin resistance (IR) imposes a significant burden on inflammatory diseases, and the triglyceride-glucose (TyG) index, which is an easily accessible indicator for detecting IR, holds great application potential in predicting the risk of arthritis. The aim of this study is to analyze the association between the TyG index and the risk of new-onset arthritis in the common population aged over 45 using a prospective cohort study design. Method This population-based cohort study involved 4418 participants from the China Health and Retirement Longitudinal Study (from Wave 1 to Wave 4). Multivariate logistic regression models were employed to investigate the association between the TyG index and new-onset arthritis, and RCS analyses were used to investigate potential non-linear relationships. Moreover, decision trees were utilized to identify high-risk populations for incident arthritis. Result Throughout a 7-year follow-up interval, it was found that 396 participants (8.96%) developed arthritis. The last TyG index quartile group (Q4) presented the highest risk of arthritis (OR, 1.39; 95% CI, 1.01, 1.91). No dose-response relationship between the TyG index and new-onset arthritis was identified (P overall=0.068, P non−linear=0.203). In the stratified analysis, we observed BMI ranging from 18.5 to 24 exhibited a heightened susceptibility to the adverse effects of the TyG index on the risk of developing arthritis (P for interaction = 0.035). Conclusion The TyG index can be used as an independent risk indicator for predicting the start of new-onset arthritis within individuals aged 45 and above within the general population. Improving glucose and lipid metabolism, along with insulin resistance, may play a big part in improving the primary prevention of arthritis.

Xueqiao Jiao, Moqi Liu, Rui Li et al.

Introduction: The present study aimed to investigate whether and how normobaric intermittent hypoxic training (IHT) or remote ischemic preconditioning (RIPC) plus normoxic training (RNT) has a synergistic protective effect on lipid metabolism and vascular function compared with normoxic training (NT) in overweight or obese adults. Methods: A total of 37 overweight or obese adults (36.03 ± 10.48 years) were randomly assigned to 3 groups: NT group (exercise intervention in normoxia), IHT group (exercise intervention in normobaric hypoxic chamber), and RNT group (exercise intervention in normoxia + RIPC twice daily). All participants carried out the same 1-h exercise intervention for a total of 4 weeks, 5 days per week. Physical fitness parameters were evaluated at pre- and postexercise intervention. Results: After training, all three groups had a significantly decreased body mass index (p &lt; 0.05). The IHT group had reduced body fat percentage, visceral fat mass (p &lt; 0.05), blood pressure (p &lt; 0.01), left ankle-brachial index (ABI), maximal heart rate (HRmax) (p &lt; 0.05), expression of peroxisome proliferator-activated receptor-γ (PPARγ) (p &lt; 0.01) and increased expression of SIRT1 (p &lt; 0.05), VEGF (p &lt; 0.01). The RNT group had lowered waist-to-hip ratio, visceral fat mass, blood pressure (p &lt; 0.05), and HRmax (p &lt; 0.01). Conclusion: IHT could effectively reduce visceral fat mass and improve vascular elasticity in overweight or obese individuals than pure NT with the activation of SIRT1-related pathways. And RNT also produced similar benefits on body composition and vascular function, which were weaker than those of IHT but stronger than NT. Given the convenience and economy of RNT, both intermittent hypoxic and ischemic training have the potential to be successful health promotion strategies for the overweight/obese population.

Sathi Sarkar, Anish Kumar Das, Satheesh Kumar Nanjappan

Yi Zhou, Meixuan Wu, Chengzhou Ouyang et al.

Biomedical research has revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). However, the generalizability and explainability are currently underemphasized. It's significant to generalize effective predictions to entities with fewer or no existing MDAs and reveal how the prediction scores are derived. In this study, our work contributes to data, model, and result analysis. First, for better formulation of the MDA issue, we integrate multi-source data into a heterogeneous graph with a broader learning and prediction scope, and we split massive verified MDAs into independent training, validation, and test sets as a benchmark. Second, we construct an end-to-end data-driven model that performs node feature encoding, graph structure learning, and binary prediction sequentially, with a heterogeneous graph transformer as the central module. Finally, computational experiments illustrate that our method outperforms existing state-of-the-art methods, achieving better evaluation metrics and alleviating the neglect of unknown miRNAs and diseases effectively. Case studies further demonstrate that we can make reliable MDA detections on diseases without MDA records, and the predictions can be explained in general and case by case.

Hao Wei, Peilun Shi, Guitao Bai et al.

Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 $mm^{2}$, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.

Juan Pablo Aparco, Haydee Cárdenas-Quintana, Eduardo Fuentes et al.

Objetivo. Determinar el cambio del gasto en alimentos ultraprocesados (AUP) en agricultores familiares del área rural del Perú, en los años 2009 y 2019. Metodología. Estudio de análisis secundario de la Encuesta Nacional de Hogares (ENAHO) del año 2009 y 2019. La población de estudio fueron los agricultores familiares del área rural del Perú. Se determinó el gasto promedio mensual en AUP consumidos dentro del hogar para el año 2009 y 2019 y se estimaron las diferencias en el gasto para el dominio rural total y los dominios geográficos costa norte, centro y sur, sierra norte, centro y sur y selva. Se usó la prueba t de Student y la prueba z de proporciones para evaluar el cambio promedio y porcentuales del gasto total destinado a la compra de AUP. Resultados. El promedio de gasto en AUP fue de S/82,56 en 2009 y de S/74,18 en 2019, esta reducción resultó significativa para el dominio Rural total y en 5 de 7 dominios (p <0,05), estos resultados fueron similares en el porcentaje del gasto total destinado a la compra de AUP. Conclusiones. Los resultados del estudio muestran que el promedio de gasto en AUP de agricultores familiares de zona rural del año 2019 tuvo una pequeña pero significativa reducción comparado con el gasto en 2009, esta tendencia se mantuvo en 5 de 7 dominios de estudio.

Daniela Asfura-Carrasco, Susana Santiago, Itziar Zazpe et al.

Abstract Objective: To investigate the association between different versions of a provegetarian food pattern (FP) and micronutrient inadequacy. Design: Cross-sectional analysis. Dietary intake was assessed at baseline through a validated 136-item FFQ. Participants were classified according to groups of different versions of a provegetarian FP: overall, healthful and unhealthful. The prevalence of inadequate intake of vitamins B1, B2, B3, B6, B12, C, A, D, E, folic acid, Zn, I, Se, Fe, Ca, K, P, Mg and Cr was evaluated using the estimated average requirement (EAR) cut-point method and the probabilistic approach. Logistic regression analyses were conducted to estimate the probability of failing to meet EAR for either ≥ 3 or ≥ 6 micronutrients. Setting: Seguimiento Universidad de Navarra (SUN) cohort. Participants: 17 825 Spanish adults. Results: Overall, subjects in the highest group of the unhealthful provegetarian FP had the highest prevalence of inadequate dietary intake for every vitamin and mineral, compared to those in the lowest group. The adjusted OR of failing to meet ≥ 3 EAR (highest v. lowest group) was 0·65 (0·54, 0·69) for the overall, 0·27 (0·24, 0·31) for the healthful and 9·04 (7·57, 10·4) for the unhealthful provegetarian FP. Conclusion: A higher adherence to an overall and healthful provegetarian FP was inversely associated with the risk of failing to meet EAR values, whereas the unhealthful version was directly associated with micronutrient inadequacy. Provegetarian FP should be well planned, prioritising nutrient-dense plant foods and minimising ultra-processed and unhealthy ones.

Franca Iorember

Patients with chronic kidney disease are at substantial risk for malnutrition, characterized by protein energy wasting and micronutrient deficiency. Studies show a high prevalence rate of malnutrition in both children and adults with chronic kidney disease. Apart from abnormalities in growth hormone-insulin like growth factor axis, malnutrition also plays a role in the development of stunted growth, commonly observed in children with chronic kidney disease. The pathogenic mechanisms of malnutrition in chronic kidney disease are complex and involve an interplay of multiple pathophysiologic alterations including decreased appetite and nutrient intake, hormonal derangements, metabolic imbalances, inflammation, increased catabolism, and dialysis related abnormalities. Malnutrition increases the risk of morbidity, mortality and overall disease burden in these patients. The simple provision of adequate calorie and protein intake does not effectively treat malnutrition in patients with chronic kidney disease owing to the intricate and multifaceted derangements affecting nutritional status in these patients. A clear understanding of the pathophysiologic mechanisms involved in the development of malnutrition in chronic kidney disease is necessary for developing strategies and interventions that are effective, and capable of restoring normal development and mitigating negative clinical outcomes. In this article, a review of the pathophysiologic mechanisms of malnutrition in chronic kidney disease is presented.

Mrinmoy Roy, Anica Tasnim Protity

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.

G. Vicente, A. Adin, T. Goicoa et al.

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of small areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal permits the analysis of big data sets and provides better results than fitting a single multivariate model.

Behzad Safarijalal, Yousef Alborzi, Esmaeil Najafi

With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. Wheat is one of the most important food resources in the world, mainly because of the variety of wheat-based products. Wheat crops are threatened by three main types of diseases which cause large amounts of annual damage in crop yield. These diseases can be eliminated by using pesticides at the right time. While the task of manually spraying pesticides is burdensome and expensive, agricultural robotics can aid farmers by increasing the speed and decreasing the amount of chemicals. In this work, a smart autonomous system has been implemented on an unmanned aerial vehicle to automate the task of monitoring wheat fields. First, an image-based deep learning approach is used to detect and classify disease-infected wheat plants. To find the most optimal method, different approaches have been studied. Because of the lack of a public wheat-disease dataset, a custom dataset has been created and labeled. Second, an efficient mapping and navigation system is presented using a simulation in the robot operating system and Gazebo environments. A 2D simultaneous localization and mapping algorithm is used for mapping the workspace autonomously with the help of a frontier-based exploration method.