High Accuracy Of Model Research Articles

Optimize the Process Parameters of The Paper Feeding Mechanism Based on The Response Surface Method

In order to solve the problem of lateral deviation during the short-distance conveying process of paper, this paper adopts the response surface method (RSM) to establish a regression model of the paper deviation amount with respect to the feed speed, pressing force and friction coefficient. The accuracy of the model is evaluated through variance analysis, fitting accuracy analysis, residual analysis and the comparison between predicted values and experimental values. The influence of various factors on the paper deviation amount is analyzed by means of three-dimensional response surfaces and contour plots. In addition, the optimal process parameters are obtained by utilizing the Numerical function in the Design-Expert software, and the reliability of the model is verified through experiments. The research results show that the optimized process parameters are as follows: the feed speed is 42.14 bag/min, the pressing force is 0.29 kg, and the friction coefficient is 0.29. The errors between the predicted values and the measured values are all within 5%, which proves the high accuracy and reliability of the regression model. This study provides a reliable theoretical basis for the optimization of the paper feeding mechanism process in the production process of environmentally friendly paper bags and has important practical application value.

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

AbstractThe surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Comparative Performance Analysis of Ensemble Models for Breast Cancer Classification

Breast cancer remains a prevalent global health issue, accounting for approximately 2.3 million new cases and 670,000 deaths worldwide in 2022. Early detection and accurate diagnosis are crucial to improving patient outcomes, as delayed identification can lead to severe complications. Advances in machine learning (ML) have facilitated improvements in cancer diagnosis, with various algorithms enhancing predictive accuracy. This study proposes a novel ensemble model for breast cancer classification, utilizing 31 features from the University of Wisconsin Breast Cancer dataset. We applied six algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—and combined them with ensemble techniques, specifically Hard Voting, to develop a high-accuracy model. The model was evaluated on classification performance metrics, achieving improvements in accuracy, precision, recall, and F1 score. Results indicate that the proposed ensemble model outperforms individual classifiers and other ensembles, showing potential as a reliable tool for early breast cancer detection.

Prediction of microstructural evolution of multicomponent polymers by Physics-Informed neural networks

As emerging concepts, leveraging physical information with machine learning to construct hybrid models enables a scientific data-driven paradigm for the investigation of complex kinetic issues in the field of polymer science. Herein, the model of physics-informed neural networks (PINNs), integrating advantages of neural networks with physics-based knowledge, is extended to predict the spatiotemporal patterns of phase-separated microstructures of multicomponent polymers. It is demonstrated that the PINN-based data-driven model can achieve the forward, backward and bidirectional predictions of spatiotemporally phase-separated patterns of homopolymer blends. All predicted patterns reveal a high accuracy and robustness of PINN-based data-driven model by leveraging a small amount of training data. Particularly, the PINN-based method can be harnessed to infer the latent physical law about the growth kinetics of phase-separated microstructures. In addition, PINN-based data-driven model can also be generalized to forecast the spatiotemporal patterns of microphase-separated nanostructures of block copolymers and infer their growth kinetics. Our study opens the possibility of learning non-equilibrium phenomena of complex polymer systems from only a few snapshots of microstructural evolution.

High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics

Van der Waals (vdW) dielectrics are promising for enhancing the performance of nanoscale field-effect transistors (FETs) based on two-dimensional (2D) semiconductors due to their clean interfaces. Ideal vdW dielectrics for 2D FETs require high dielectric constants and proper band alignment with 2D semiconductors. However, high-quality dielectrics remain scarce. Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. High-throughput first-principles calculations yielded bandgaps and dielectric properties of 189 0D, 81 1D and 252 2D vdW materials. Among which, 9 highly promising dielectric candidates are suitable for MoS2-based FETs. Element prevalence analysis indicates that materials containing strongly electronegative anions and heavy cations are more likely to be promising dielectrics. Moreover, we developed a high-accuracy two-step machine learning (ML) classifier for screening dielectrics. Implementing active learning framework, we successfully identified 49 additional promising vdW dielectrics. This work provides a rich candidate list of vdW dielectrics along with a high-accuracy ML screening model, facilitating future development of 2D FETs.

Transfer learning enabled transformer-based generative adversarial networks for modeling and generating terahertz channels

Terahertz communications are envisioned as a promising technology for the sixth generation and beyond wireless systems, which can support wireless links with Terabits-per-second (Tbps) data rates. As the foundation of designing terahertz communications, channel modeling and characterization are crucial to scrutinize the potential of this spectrum. However, current channel modeling in the terahertz band heavily relies on time-consuming and costly measurements. Here, we propose a transfer learning enabled transformer based generative adversarial network to mitigate this problem in terahertz channel modeling. Specifically, as a fundamental building block, a generative adversarial network is exploited to generate channel parameters. To improve the accuracy, a transformer structure with a self-attention mechanism is incorporated in generative adversarial network. Still incurring errors compared with ground-truth measurement, a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed method can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complement of current channel modeling techniques.

ANCF-based modelling of rigid-flexible coupling of helicopter flexible rotor blades

Abstract A high-precision rotor structure dynamic model applicable to a variety of helicopter blade hub structures is established considering rotor blade flexible deformation. The absolute nodal coordinate method is applied to the traditional rotor blade rigid-flexible coupling dynamics model, making the new model applicable to both fully articulated rotor blades and other advanced blade hub structures. No small-volume assumptions were made in the modelling process. Both Green’s strain expression under finite deformation and the geometrical relation of elastic deformation motion based on the absolute nodal coordinate method are used to derive the blade strain energy, kinetic energy, and the external load virtual work. The rotation angle at the articulation constraint at the root of the blade coupled with the elastic deformation degrees of freedom of the blade in three independent rigid body degrees of freedom, respectively. The rotor blade rigid-flexible coupling dynamics equations were established according to the Hamilton action principle. The time-domain simulation is carried out under a free single pendulum and fixed rotational speed/fixed torque for the established flexible blade model, which verifies the validity and high accuracy of the rigid-flexible coupling articulation model of the flexible rotor blade and the blade hub.

Optimizing concrete rheology to mitigate aerosol pollutants in wet-mix shotcrete processes

Studies focusing on optimizing the pumpability and sprayability of concrete exist; however, research addressing the control of aerosol pollutants during the spraying process is limited. This study investigated the intricate relationships among additive formulations, rheology properties of fresh mixes, and aerosol pollutant emissions using a Box–Behnken design, combined with response surface methodology and gray correlation coefficient analysis. The developed high-accuracy regression model offers valuable insights into the impact of water-reducing agents (WRA), air-entraining agents (AEA), thickening agents (TA), and defoaming agents (DA) on the zeta potential, slump, surface tension of fresh mix concrete, and spread angle during wet-mix shotcrete (WMS), with R2 values of 0.94, 0.95, 0.95, and 0.95, respectively. Additionally, by considering both sprayability and a low spread angle, this approach facilitated the development of an optimal composite incorporating 1.36 % WRA, 1.50 % AEA, 0.20 % TA, and 1.00 % DA, yielding desired properties, including a slump of 200 mm, spread angle of 20°, and aerosol pollutant concentration of 21 mg/m3. This study provides a model to aid the formulation of shotcrete that mitigates aerosol pollutant generation during the WMS process, thereby contributing to the broader objectives of environmental sustainability and workplace safety.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.

A comparative study on the Lattice Boltzmann Method and the VoF-Continuum method for oxygen transport in the anodic porous transport layer of an electrolyzer

The optimization of Polymer Electrolyte Membrane (PEM) electrolyzers necessitates an intimate knowledge of the oxygen flows within the anodic porous transport layers (PTLs) to determine any possible reduction in performance. In this field, as experimental studies are cumbersome and expensive, numerical modeling has arisen as a viable alternative for studying the oxygen transport within the Anodic PTLs of a PEM electrolyzer. Amongst the various numerical modeling techniques, the Lattice Boltzmann Method (LBM) is gaining prominence for its effectiveness in analyzing fluid transport within porous media due to its mesoscopic nature and ease of implementation. This study utilizes the Shan-Chen LBM methodology to model the flow of oxygen within the Anodic PTL of a PEM electrolyzer and compares it against the Volume-of-Fluid-based Continuum Model. The results show that LBM can not only replicate the experimental studies accurately, but can also maintain its high accuracy at progressively shrinking length scales of PTLs, even at length scales where the VoF-based Continuum Model would run into accuracy issues. The high accuracy of the LBM model, combined with the simplicity of the LB algorithm makes LBM a powerful technique for simulating the microfluidic flows such as the flow of oxygen within the Anodic PTL of a PEM electrolyzer.

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Groundwater nitrate contamination poses a potential threat to human health and environmental safety globally. This study proposes an interpretable stacking ensemble learning (SEL) framework for enhancing and interpreting groundwater nitrate spatial predictions by integrating the two-level heterogeneous SEL model and SHapley Additive exPlanations (SHAP). In the SEL model, five commonly used machine learning models were utilized as base models (gradient boosting decision tree, extreme gradient boosting, random forest, extremely randomized trees, and k-nearest neighbor), whose outputs were taken as input data for the meta-model. When applied to the agricultural intensive area, the Eden Valley in the UK, the SEL model outperformed the individual models in predictive performance and generalization ability. It reveals a mean groundwater nitrate level of 2.22 mg/L-N, with 2.46% of sandstone aquifers exceeding the drinking standard of 11.3 mg/L-N. Alarmingly, 8.74% of areas with high groundwater nitrate remain outside the designated nitrate vulnerable zones. Moreover, SHAP identified that transmissivity, baseflow index, hydraulic conductivity, the percentage of arable land, and the C:N ratio in the soil were the top five key driving factors of groundwater nitrate. With nitrate threatening groundwater globally, this study presents a high-accuracy, interpretable, and flexible modeling framework that enhances our understanding of the mechanisms behind groundwater nitrate contamination. It implies that the interpretable SEL framework has great promise for providing valuable evidence for environmental management, water resource protection, and sustainable development, particularly in the data-scarce area.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

Prospects for rank-reduced CCSD(T) in the context of high-accuracy thermochemistry.

Obtaining sub-chemical accuracy (1 kJ mol-1) for reaction energies of medium-sized gas-phase molecules is a longstanding challenge in the field of thermochemical modeling. The perturbative triples correction to coupled-cluster single double triple [CCSD(T)] constitutes an important component of all high-accuracy composite model chemistries that obtain this accuracy but can be a roadblock in the calculation of medium to large systems due to its O(N7) scaling, particularly in HEAT-like model chemistries that eschew separation of core and valence correlation. This study extends the work of Lesiuk [J. Chem. Phys. 156, 064103 (2022)] with new approximate methods and assesses the accuracy of five different approximations of (T) in the context of a subset of molecules selected from the W4-17 dataset. It is demonstrated that all of these approximate methods can achieve sub-0.1 kJ mol-1 accuracy with respect to canonical, density-fitted (T) contributions with a modest number of projectors. The approximation labeled Z̃T appears to offer the best trade-off between cost and accuracy and shows significant promise in an order-of-magnitude reduction in the computational cost of the CCSD(T) component of high-accuracy model chemistries.

SecureEI: Proactive intellectual property protection of AI models for edge intelligence

Deploying AI models on edge computing platforms enhances real-time performance, reduces network dependency, and ensures data privacy on terminal devices. However, these advantages come with increased risks of model leakage and misuse due to the vulnerability of edge environments to physical and cyber attacks compared to cloud-based solutions. To mitigate these risks, we propose SecureEI, a proactive intellectual property protection method for AI models that leverages model splitting and data poisoning techniques. SecureEI divides the model into two components: DeviceNet, which processes input data into protected license data, and EdgeNet, which operates on the license data to perform the intended tasks. This method ensures that only the transformed license data yields high model accuracy, while original data remains unrecognizable, even under fine-tuning attacks. We further employ targeted training strategies and weight adjustments to enhance the model’s resistance to potential attacks that aim to restore its recognition capabilities for original data. Evaluations on MNIST, Cifar10, and FaceScrub datasets demonstrate that SecureEI not only maintains high model accuracy on license data but also significantly bolsters defense against fine-tuning attacks, outperforming existing state-of-the-art techniques in safeguarding AI intellectual property on edge platforms.

Estimation of Trip-based Generation Models and Calibration of Mode Choice Models for the American Travel Behavior

Background The National Household Travel Survey (NHTS) is the primary national dataset for analyzing personal and household travel trends. It covers daily non-commercial travel across all modes and details information on travelers, their households, and their vehicles. Materials and Methods The NHTS includes critical data for calibrating travel demand models, such as vehicle details (number and type), individual demographic characteristics (age, gender, employment), and household demographics (income, ownership status, size, race). In this study, we utilized the 2017 NHTS summary statistics to estimate individual trip generation models, differentiating between weekdays and weekends. By comparing factors influencing daily person trips during these periods, we aimed to discern distinctions between the two models. Additionally, we calibrated a mode choice model to understand the impact of trip purpose, duration, household income, and the ratio of available vehicles to household size on the chosen mode. Our analysis focused on identifying and quantifying the factors influencing travel behavior, providing insights into how various variables affect the number of trips and mode choices. Results The results indicated variations between weekday and weekend models, with the presence of non-workers and individuals' education levels emerging as crucial factors for weekday travel. Conversely, the existence of children, household income level, and personal yearly miles driven were identified as significant factors affecting weekend travel. Additionally, common characteristics such as household size and urban residence were substantial in both models. The Multinomial regression analysis investigated the correlations between individual, household, activity, and trip characteristics and the modes of transportation selected by travelers. The most significant factors influencing an individual's mode choice are household income, the ratio of available vehicles to household size, and activity purpose. Discussion The study compared weekday and weekend travel behavior using trip-based generation models. Weekday travel was significantly influenced by non-workers and education levels, while weekend travel was more affected by factors like the presence of children, household income, and annual kilometers traveled. Both models emphasized the role of household size and urban residence in shaping travel patterns. The research also examined transportation mode choice, with validation confirming the high accuracy and robustness of the mode choice model in predicting travel behavior. Conclusions The study findings are valuable to transportation planners, policymakers, and urban mobility experts aiming to enhance the efficiency and effectiveness of transportation systems. By offering a detailed understanding of individual and household travel patterns, the research enables data-driven interventions that support policy decisions, such as optimizing transit routes, enhancing infrastructure for active transportation, and managing congestion.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

Hysteresis dynamic modeling of 4-SPS parallel all-metallic isolator with spherical joints considering nonlinear micro-collision and interfacial friction

This work aims to identify ways to model a high-accuracy hysteresis dynamic model for an innovative 4-SPS parallel all-metallic isolator. Firstly, a three-dimensional contact model of spherical joints under different conditions (stretching and compression) is proposed, referred to as the integrated model of nonlinear micro-collision and interfacial friction (INCF model). Simultaneously, in conjunction with the nonlinear elastic recovery force, nonlinear damping force, and nonlinear hysteresis damping force, the high-accuracy hysteresis dynamic model of the isolator is constructed. To validate the accuracy, dynamic experiments are conducted on the isolator at distinct frequencies (6–9 Hz) and amplitudes (0.6–0.9 mm). The results indicate that the hysteresis dynamic model constructed based on the INCF model exhibits a remarkably high level of precision compared to the classic model (R2=0.998). This increased accuracy is attributed to the consideration of influencing factors of the INCF model, such as micro-collisions between spherical joints and interfacial friction during the operation of the isolator. These variables are determined by the material properties and geometric dimensions of the spherical joint and can be adjusted in real time based on the isolator deformations to enhance the model's accuracy. The method of parameter identification applied to overall structure resolves challenge of measuring the internal deformation of spherical joints. Importantly, the INCF model is not limited to the isolators proposed in this work but can also be applied to similar isolators with Stewart structure-type connections that employ spherical joints. These research findings provide a robust theoretical support for the design and performance optimization of the isolator, with the potential to positively impact related engineering applications.

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.