Scholarly Works, Biological Systems Engineering

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Research articles, presentations, and other scholarship

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Agrivoltaics Policy Frameworks in the United States: Selected Policies and Programs Through 2024
Akbari, Pardis; Hall, Ralph P.; Ignosh, John (Virginia Tech, 2026-01-30)
Extreme Weather Events and Risk Communication Challenges in Central Appalachia: A Qualitative Inquiry
Khan, Azmat; Chadwick, Amy E.; Kruse-Daniels, Natalie; Dabelko, Geoffrey D.; Krometis, Leigh Anne H.; Shinn, Jamie E.; Lynch, Amy J.; Garner, Emily; Hession, W. Cully; Bowman, Jen (2025-03-01)
This study inventories and identifies communication challenges faced by emergency management agencies in Central Appalachia as they engage communities in preparation, response and recovery efforts for extreme weather events (EWEs). Drawing on data from nine group discussions and guided by the Social Ecological Model, the analysis discerned an array of barriers to effective risk communication, originating from cultural, organizational, interpersonal and individual dynamics. It was found that a pervasive distrust of emergency agencies and broader climate governance, articulated through the notion of ‘mining,’ undermines organizational legitimacy. Conflicting messages from emergency sources with ambiguous or overlapping roles create confusion, numb and desensitize populations, and further erode source credibility. Poor internet and cellular connectivity constrain timely information delivery and exacerbate vulnerabilities. Additionally, the region's ingrained culture of ‘riding‐it‐out’, while a valuable source of organic resilience and self‐efficacy, is seen by some emergency managers as ‘stubbornness,’ which leads to misalignment in risk communication. This study re‐contextualizes these cultural attributes as essential ‘social capital’ and offers strategies to align communication practices and resources with local identity and agency needs. Findings contribute to culturally responsive approaches to participatory risk communication.
Comparing in-home and bottled drinking water quality: regulated and emerging contaminants in rural Central Appalachia
Albi, Kate; Krometis, Leigh-Anne H.; Ling, Erin; Cohen, Alasdair; Xia, Kang; Gray, Austin D.; Dudzinski, Emerald; Ellis, Kimberly P. (IWA Publishing, 2025-09)
An increasing number of Americans rely on bottled water for household use, citing perceptions of poor in-home water quality and/or distrust of public water utilities. We analyzed in-home (n = 23), roadside spring (n = 4), and bottled drinking water (n = 36) in Central Appalachia. All samples were analyzed for regulated (bacteria, inorganic ions) and emerging (PFAS, microplastics) contaminants. Study survey results indicated the majority (83%) of participants viewed their in-home water quality as satisfactory or poor due to negative organoleptic perceptions. Coliform bacteria and sodium levels exceeding recommended levels were detected in 52% of home water samples, though detections varied by source, i.e., high sodium was more often observed in municipal water, while bacteria were more often observed in private system water. Bottled water samples did not exceed any regulations, though median microplastic concentrations were statistically higher (p = 0.001, Wilcoxon rank-sum test) than those recovered from in-home samples. PFAS compounds were detected in some in-home and bottled water samples at very low levels. While in general bottled water appears to be a safe drinking water source in these areas, the associated costs in time and money for lower-income households are considerable, and were estimated by participants as $68–400/month.
AI-Driven Livestock Biosensing for Prediction of Metabolic Diseases
Ali, Md Azahar; Kachouei, Matin Ataei (IEEE, 2025)
We report the development of a highly sensitive 3D-printed sensor for the on-farm, early detection of subclinical hypocalcemia (SHC) in dairy cows. The printed 3D sensing structure incorporates periodic micropatterns of ion-to-electron polymer-based transducing layer that enhances sensitivity when analyzing milk samples. This novel sensor detects radiometric targets of calcium (Ca²⁺) and phosphate (PO4^2-) in milk, enabling the identification of SHC in under a minute. We apply regression models, including k Nearest Neighbors (k-NN) and Logistic Regression, to predict livestock health, evaluating performance through accuracy, area under the curve (AUC), and confusion matrices. Unlike traditional tests, this sensor provides dairy farmers with a tool to monitor the health of transition dairy cows.
Micro-Nano Hybrid Architectures for Sub-Nanogram Detection of Avian Influenza H5N1
Kachouei, Matin Ataei; Jacobs, Leonie; Ha, Dong Sam; Ali, Md Azahar (IEEE, 2025)
The ongoing spread of the highly pathogenic avian influenza H5N1 virus has caused severe disruptions in the poultry industry, leading to economic losses and raising concerns about cross-species transmission. Recent outbreaks in mammals increase the risk of zoonotic spillover, making rapid and sensitive virus detection crucial for effective containment and management. We report here a low-cost, lithography-free biosensor incorporating graphene oxide, silver nanowires, and self-assembled monolayer as micro-nano hybrid transducers for the detection of H5N1 hemagglutinin. The sensor achieved a detection limit of 40 picograms per mL. We have also manufactured a fully 3D-printed micropillar array-based sensor and evaluated its performance as a viral sensor against traditional 2D planar electrodes. These printed sensors will be useful for on-farm poultry testing, providing a practical solution for early virus detection and control.
AI-Powered Nanosensing of Lactate in Dairy Cows
Kachouei, Matin Ataei; Chick, Shannon; Ali, Md Azahar (IEEE, 2025)
Early detection of metabolic diseases, including lactic acidosis, is crucial for effective livestock health management. This study presents the development of a nanosensor platform using graphene nanosheets and lactate oxidase (LOx) enzyme to detect lactate and hydrogen peroxide (H2O2) concentrations within a minute. Machine learning (ML) techniques, including polynomial regression and random forest (RF) regression, were used to optimize sensor calibration. Polynomial regression (degrees 3 and 4) achieved perfect accuracy (r²=1.00), while RF regression demonstrated strong predictive performance (r²=0.857). These results underscore the lactate sensor's potential for precise, reliable detection in complex biological fluids, providing an advantage over traditional methods in dairy cattle health monitoring.
Nanosensing of Hepatitis E Virus in Swine Using Graphene
Chick, Shannon; Ataei Kachouei, Matin; Knowlton, Katharine; Meng, Xiang-Jin; Ali, Md. Azahar (IEEE, 2025-07-15)
Sensing of the hepatitis E virus is crucial for effective porcine health management and prevention of spread to humans. This study presents the development of a nanosensor using graphene nanosheets to detect hepatitis E antigen within a minute. The graphene layer not only increases the loading of antibodies specific to the hepatitis E virus but also enhances sensitivity and selectivity. This sensor is sensitive to 10 fM of hepatitis E antigen. This nanosensor holds significant potential for the rapid and early detection and monitoring of hepatitis E, thereby contributing to enhanced public health outcomes and the safety of pork products.
3D-Printed Wearable Biosensors for Livestock Health Monitoring
Ali, Md Azahar; Howell, Brittany R.; Zhang, Liqing (IEEE, 2025-07)
Livestock health monitoring stands as a linchpin in ensuring both the welfare of animals and the optimization of productivity. As we navigate toward meeting current and future food crises, the role of biosensors in this context cannot be overstated. Such biosensors serve as indispensable tools, offering real-time insights into the health status of livestock, thereby enabling early detection of diseases and prompt intervention. In addressing the challenges and potential of biosensors for livestock sensing, it is clear that while biosensors have seen extensive use in human health monitoring, their application in livestock is crucial for ensuring animal well-being and productivity, vital in meeting global food demands. To maximize effectiveness, there is a need for advanced manufacturing to develop customized, user-friendly, and cost-effective sensors. By harnessing the synergistic potential of electrochemical biosensors and advanced manufacturing, this review discusses the challenges that currently impede the widespread adoption of wearable electrochemical biosensors, advanced manufacturing techniques, and artificial intelligence in livestock sensing. This strategic approach not only bolsters animal welfare and productivity but also fortifies agricultural resilience in the face of evolving global food demands. This review highlights recent advancements in biosensors for livestock monitoring.
The pH-Dependent Specificity of Cathepsin S and Its Implications for Inflammatory Communications and Disease
DeHority, Riley; Gil Pineda, Laura I.; Cochran, Kari; Chen, Bentley; Bratek, Daniel; Helm, Richard F.; Lemkul, Justin A.; Zhang, Chenming (American Chemical Society, 2025-09-16)
Proteases have two major roles in health and disease: making functional changes to proteins as a post-translational modification and degradation of proteins as a regulatory or waste management mechanism. The cysteine protease cathepsin S serves both of these functions. It digests antigens in the adaptive immune system and is associated with many autoimmune diseases and cancers. Here, we show that the catalytic specificity of human cathepsin S is regulated by the pH conditions of its environment and identify the structural determinants of this switch. Peptide digests show that the proteolytic specificity of cathepsin S narrows at extracellular pH. Crystal structures reveal that a lysine residue descends into the S3 pocket of the active site above pH 7, which can be explained by changes in the protein's surface charge at that pH. We discuss biological compartment transitions and disease processes associated with cathepsin S in which these pH-dependent specificity switches may be triggered.
Evaluating the Effectiveness of Machine Learning for Alzheimer’s Disease Prediction Using Applied Explainability
Huang, Chih-Hao; Batarseh, Feras A.; Ullah, Aman (MDPI, 2025-11-12)
Early and accurate diagnosis of Alzheimer’s disease (AD) is critical for patient outcomes yet presents a significant clinical challenge. This study evaluates the effectiveness of four machine learning models—Logistic Regression, Random Forest, Support Vector Machine, and a Feed-Forward Neural Network—for the five-class classification of AD stages. We systematically compare model performance under two conditions, one including cognitive assessment data and one without, to quantify the diagnostic value of these functional tests. To ensure transparency, we use SHapley Additive exPlanations (SHAPs) to interpret the model predictions. Results show that the inclusion of cognitive data is paramount for accuracy. The RF model performed best, achieving an accuracy of 84.4% with cognitive data included. Without this, performance for all models dropped significantly. SHAP analysis revealed that in the presence of cognitive data, models primarily rely on functional scores like the Clinical Dementia Rating—Sum of Boxes. In their absence, models correctly identify key biological markers, including PET (positron emission tomography) imaging of amyloid burden (FBB, AV45) and hippocampal atrophy, as the next-best predictors. This work underscores the indispensable role of cognitive assessments in AD classification and demonstrates that explainable AI can validate model behavior against clinical knowledge, fostering trust in computational diagnostic tools.
An AI-Enabled System for Automated Plant Detection and Site-Specific Fertilizer Application for Cotton Crops
Chouriya, Arjun; Soni, Peeyush; Chandel, Abhilash K.; Patel, Ajay Kumar (MDPI, 2025-10-08)
Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage.
Linking Runoff Source Areas and Nitrogen Fluxes Across Topographic and Land Use Gradients
Easton, Zachary M. (American Society of Agricultural and Biological Engineers, 2025)
Land use in urban areas can alter natural hydrologic and chemical processes and make distinguishing the water quality impact of these land uses difficult. To quantify how hydrological and chemical processes are altered in urban areas, runoff was collected from 213 storm events over a six-year period, from three land uses and nine topographic positions, and analyzed for runoff, ammonium (NH4+-N), and nitrate (NO3--N) flux using linear mixed effects models. Monitored land uses included fertilized lawns (FL), low maintenance (LM) areas, and urban wooded (FR) areas. Stream gages, installed at the entrance to the urban area and the watershed outlet, monitored the impact of the urban area on integrated stream water quality. Analysis revealed that the FL land use had higher N loss in areas with convergent topography, shallow soil, and higher wetness. In contrast, the FL had lower N loss than the LM or FR land uses in areas with deeper soil, less contributing area, and less runoff. Streamflow in the urban area increased under storm conditions and decreased under dry conditions compared to the undeveloped upper area of the watershed. NH4+-N loads measured at the stream gauges indicated that the urban area, in aggregate, acted as an NH4+-N sink, as evidenced by higher normalized loads from the forested upper watershed than after the stream flowed through the urban area. In contrast, NO3--N loads increased substantially in the urban area of the watershed. These results show that urban land uses can both contribute to and mitigate nitrogen losses depending on site conditions.
Evaluation of surface water supply impacts from permit exemptions: A comparison with climate change and demand growth
Sangha, Laljeet; Hildebrand, Daniel; Scott, Durelle T.; Shortridge, Julie (Wiley, 2024-06-01)
Many states in the Eastern U.S. have limited water withdrawal regulations, posing significant risks to water supply management during periods of low flows. While these states require water withdrawal permits, exemptions for grandfathered withdrawals that allow unregulated access to surface water are common. Such permit exemptions present a challenge to water supply management, as full utilization of allowable withdrawals by permit-exempt users could pose risks to maintaining adequate water supplies for current and projected demand. This study used reported permit exemption data in Virginia to understand the extent, volume, and potential impact of permit-exempt withdrawals on 30- and 90-day low flows. The permit-exempt withdrawal values used in this study were obtained from Virginia Department of Environmental Quality. Maximum permit-exempt withdrawal volumes were significantly higher than projected future demands in permitted users. The impacts of these withdrawals on drought flows were compared with the impacts presented by climate change and demand growth. Widespread reduction in flows was observed with the "dry" future climate change scenario, while impacts were more localized in the exempt users and the demand growth scenarios. The impacts of exempt users exceeded the impact of climate change and demand growth scenarios in many regions during low-flow periods. Therefore, more comprehensive water planning, policy and research is needed to address the impact of permit exemptions.
A Comprehensive Review of Sensing, Control, and Networking in Agricultural Robots: From Perception to Coordination
Nkwocha, Chijioke Leonard; Adewumi, Adeayo; Folorunsho, Samuel Oluwadare; Eze, Chrisantus; Jjagwe, Pius; Kemeshi, James; Wang, Ning (MDPI, 2025-10-29)
This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture.
Manganese exposure from spring and well waters in the Shenandoah Valley: interplay of aquifer lithology, soil composition, and redox conditions
Hinkle, Margaret A. G.; Ziegler, Brady; Culbertson, Haley; Goldmann, Christopher; Croy, Marina E.; Willis, Noah; Ling, Erin; Reinhart, Benjamin; Lyon, Eva C. (Springer, 2024-06-01)
Manganese (Mn) is of particular concern in groundwater, as low-level chronic exposure to aqueous Mn concentrations in drinking water can result in a variety of health and neurodevelopmental effects. Much of the global population relies on drinking water sourced from karst aquifers. Thus, we seek to assess the relative risk of Mn contamination in karst by investigating the Shenandoah Valley, VA region, as it is underlain by both karst and non-karst aquifers and much of the population relies on water wells and spring water. Water and soil samples were collected throughout the Shenandoah Valley, to supplement pre-existing well water and spring data from the National Water Information System and the Virginia Household Water Quality Program, totaling 1815 wells and 119 springs. Soils were analyzed using X-ray fluorescence and Mn K-Edge X-ray absorption near-edge structure spectroscopy. Factors such as soil type, soil geochemistry, and aquifer lithology were linked with each location to determine if correlations exist with aqueous Mn concentrations. Analyzing the distribution of Mn in drinking water sources suggests that water wells and springs within karst aquifers are preferable with respect to chronic Mn exposure, with < 4.9% of wells and springs in dolostone and limestone aquifers exceeding 100 ppb Mn, while sandstone and shale aquifers have a heightened risk, with > 20% of wells exceeding 100 ppb Mn. The geochemistry of associated soils and spatial relationships to various hydrologic and geologic features indicates that water interactions with aquifer lithology and soils contribute to aqueous Mn concentrations. Relationships between aqueous Mn in spring waters and Mn in soils indicate that increasing aqueous Mn is correlated with decreasing soil Mn(IV). These results point to redox conditions exerting a dominant control on Mn in this region.
Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics
Sun, Yue; Nayani, Nikhita Sai; Xu, Yixiang; Xu, Zhanfeng; Yang, Jun; Feng, Yiming (American Chemical Society, 2024-06-03)
Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky-Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model's predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry.
Quantifying water effluent violations and enforcement impacts using causal AI
Wang, Yingjie; Sobien, Dan; Kulkarni, Ajay; Batarseh, Feras A. (Wiley, 2024-06-01)
In the landscape of environmental governance, controlling water pollution through the regulation of point sources is vital as it preserves ecosystems, protects human health, ensures legal compliance, and fulfills global environmental responsibilities. Under the Clean Water Act, the integrated compliance information system monitors the compliance and enforcement status of facilities regulated by the National Pollutant Discharge Elimination System (NPDES) permit program. This study assesses temporal and geographic trends for effluent violations within the United States and introduces a novel metric for quantifying violation trends at the facility level. Furthermore, we utilize a linear parametric approach for Conditional Average Treatment Effect (CATE) causal analysis to quantify the heterogeneous effects of EPA and state enforcement actions on effluent violation trends at facilities with NPDES permits. Our research reveals insights into national pollutant discharge trends, regional clustering of all pollutant violation types in Ohio (G(i)* Z-score of 2.15), and priority pollutants in West Virginia (G(i)* Z-score of 3.07). The trend metric identifies regulated facilities that struggle with severe and recurring violations. The causal model highlights variations in state compliance and enforcement effectiveness, underscoring the successful moderation of violation trends by states such as Montana and Maryland, among others.
Genetically Encoded, Noise-Tolerant, Auxin Biosensors in Yeast
Chaisupa, Patarasuda; Rahman, Md Mahbubur; Hildreth, Sherry B.; Moseley, Saede; Gatling, Chauncey; Bryant, Matthew R.; Helm, Richard F.; Wright, R. Clay (American Chemical Society, 2024-08-28)
Auxins are crucial signaling molecules that regulate the growth, metabolism, and behavior of various organisms, most notably plants but also bacteria, fungi, and animals. Many microbes synthesize and perceive auxins, primarily indole-3-acetic acid (IAA, referred to as auxin herein), the most prevalent natural auxin, which influences their ability to colonize plants and animals. Understanding auxin biosynthesis and signaling in fungi may allow us to better control interkingdom relationships and microbiomes from agricultural soils to the human gut. Despite this importance, a biological tool for measuring auxin with high spatial and temporal resolution has not been engineered in fungi. In this study, we present a suite of genetically encoded, ratiometric, protein-based auxin biosensors designed for the model yeast Saccharomyces cerevisiae. Inspired by auxin signaling in plants, the ratiometric nature of these biosensors enhances the precision of auxin concentration measurements by minimizing clonal and growth phase variation. We used these biosensors to measure auxin production across diverse growth conditions and phases in yeast cultures and calibrated their responses to physiologically relevant levels of auxin. Future work will aim to improve the fold change and reversibility of these biosensors. These genetically encoded auxin biosensors are valuable tools for investigating auxin biosynthesis and signaling in S. cerevisiae and potentially other yeast and fungi and will also advance quantitative functional studies of the plant auxin perception machinery, from which they are built.
A methodological framework for assessing sea level rise impacts on nitrate loading in coastal agricultural watersheds using SWAT plus : A case study of the Tar-Pamlico River basin, North Carolina, USA
Tapas, Mahesh R.; Etheridge, Randall; Tran, Thanh-Nhan-Duc; Finlay, Colin G.; Peralta, Ariane L.; Bell, Natasha; Xu, Yicheng; Lakshmi, Venkataraman (Elsevier, 2024-11-15)
This study addresses the urgent need to understand the impacts of climate change on coastal ecosystems by demonstrating how to use the SWAT+ model to assess the effects of sea level rise (SLR) on agricultural nitrate export in a coastal watershed. Our framework for incorporating SLR in the SWAT+ model includes: (1) reclassifying current land uses to water for areas with elevations below 0.3 m based on SLR projections for midcentury; (2) creating new SLR-influenced land uses, SLR-influenced crop database, and hydrological response units for areas with elevations below 2.4 m; and (3) adjusting SWAT+ parameters for the SLR-influenced areas to simulate the effects of saltwater intrusion on processes such as plant yield and denitrification. We demonstrate this approach in the Tar-Pamlico River basin, a coastal watershed in eastern North Carolina, USA. We calibrated the model for monthly nitrate load at Washington, NC, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.61. Our findings show that SLR substantially alters nitrate delivery to the estuary, with increased nitrate loads observed in all seasons. Higher load increases were noted in winter and spring due to elevated flows, while higher percentage increases occurred in summer and fall, attributed to reduced plant uptake and disrupted nitrogen cycle transformations. Overall, we observed an increase in mean annual nitrate loads from 155,000 kg NO3-N under baseline conditions to 157,000 kg NO3-N under SLR scenarios, confirmed by a statistically significant paired t-test (p = 2.16 x 10(-10)). This pioneering framework sets the stage for more sophisticated and accurate modeling of SLR impacts in diverse hydrological scenarios, offering a vital tool for hydrological modelers.
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
Nkwocha, Chijioke Leonard; Chandel, Abhilash Kumar (MDPI, 2025-10-16)
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.

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