Search Results (5,388)

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

Background: The ability of preseason screening to identify athletes at risk for non-contact lower-limb injury remains uncertain. Purpose: To determine whether knee and ankle joint tests, body mass index (BMI), prior lower-limb injury, and Functional Movement Screen (FMS) scores were associated with injury and improved discrimination. Methods: Prospectively collected data from 381 collegiate athletes across 11 sports were retrospectively analyzed. Preseason assessments included BMI, injury history, FMS composite and subtest scores, and knee and ankle special tests summarized regionally. The primary outcome was two or more non-contact lower-limb injuries during the subsequent season; at least one injury was secondary. Hierarchical multivariable logistic regression, sex-stratified and sensitivity analyses, and cross-validated ridge logistic regression were performed. Results: Eighty athletes (21.0%) sustained at least one injury and 41 (10.8%) sustained two or more. Prior injury was associated with ≥2 injuries in Models 1 and 2 (odds ratios = 2.03 and 2.00, respectively) and remained the most consistent correlate across sensitivity analyses. Knee and ankle test findings and FMS composite scores were not consistently associated with injury in primary analyses. Exploratory subgroup associations included female BMI, In-Line Lunge, female Deep Squat, and male ankle instability without prior injury. For the primary outcome, apparent AUC was 0.621–0.649 and cross-validated AUC was 0.535–0.562; adding FMS variables did not improve cross-validated discrimination. Conclusions: Prior injury history was the most consistent risk marker, but the preseason battery showed limited generalizable predictive value. FMS and joint-test measures should not be used alone to predict individual injury risk. Full article

This paper develops a Banach space-valued approximation framework based on symmetrized neural network (SNN) operators generated by a deformation-dependent sigmoidal activation function. Symmetry is introduced directly at the activation level through a reciprocal-deformation mechanism, yielding a positive, even, normalized, and localized density kernel satisfying the partition of unity. The resulting construction provides normalized compact-interval and whole-line quasi-interpolation operators for Banach space-valued functions. Quantitative pointwise and uniform convergence estimates are established through the first modulus of continuity and are extended to higher-order and Caputo–Bochner fractional approximation. Numerical diagnostics support the theoretical kernel properties, and fractional approximation experiments compare the SNN and classical NN operators under common computational conditions. A controlled blind-prediction experiment on a synthetic monthly temperature-like series uses a strict fit–validation–test protocol and a parameter-matched operator comparison, with seasonal ARIMA and MLP models as external baselines. Across five independent realizations, the SNN attains the best mean predictive performance, with $R^2=0.9500$, NMAE $=0.0452$, and NRMSE $=0.0570$. A vector-valued experiment in $Y={\mathbb{R}}^2$ further illustrates the non-scalar applicability of the Banach space framework. In addition, the normalized SNN kernel weights provide an intrinsic node-level interpretation mechanism without requiring an external post hoc explainability method. Full article

This study proposes a multi-sensor time-series framework for estimating sugarcane planting Days After Planting (DAP) using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery in Phu Khiao District, Chaiyaphum Province, Thailand. The framework integrates vegetation indices, SAR backscatter, Dynamic Time Warping Barycenter Averaging (DBA), Derivative Dynamic Time Warping (DDTW), and stage-specific Ordinary Least Squares (OLS) calibration to estimate planting DAP and crop age. Sugarcane fields were first identified using a Random Forest classifier trained on combined multispectral and SAR features, achieving an Overall Accuracy of 88.5% and a Kappa coefficient of 0.82 for the optimal feature configuration. Multi-temporal vegetation index and SAR backscatter time series were then smoothed using Locally Weighted Scatterplot Smoothing (LOWESS) and aligned with phenological reference prototypes generated by DBA using DDTW. Stage-specific OLS models were subsequently applied to reduce systematic prediction bias. The calibrated framework achieved a coefficient of determination (R²) of 0.9970 and a root mean square error (RMSE) of 5.21 days, representing a substantial improvement over the uncalibrated DDTW estimates (R² = 0.9953, RMSE = 7.00 days). DDTW alignment produced the highest accuracy during the grand growth stage (Stage 2), with normalized RMSE (NRMSE) ranging from 0.064 to 0.091 across individual features. Independent validation using 140 sugarcane plots from the 2024/2025 cropping season demonstrated the plausibility of the proposed framework, correctly identifying Stage 3 (sugar accumulation) growth for 98.6% of the plots and estimating a mean planting DAP of 267.06 ± 9.55 days. These findings demonstrate that the proposed framework provides an accurate and operational approach for estimating sugarcane planting dates from satellite time-series data, supporting crop age monitoring and harvest planning in tropical agricultural regions where field-based planting records are unavailable or incomplete. Full article

Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. Full article

Accurate wind speed forecasting plays a crucial role in the safe and stable operation of wind farms and the efficient integration of renewable energy into modern power systems. However, wind speed exhibits complex spatiotemporal variations affected by diverse meteorological conditions, making high-precision prediction a long-standing bottleneck in wind power scheduling. This paper develops a U-shaped spatiotemporal feature fusion network named U-STNet, which realizes joint modeling of inter-turbine spatial correlations and multi-period long-range temporal dependencies. The model maps raw wind speed series to high-dimensional embeddings and adopts an encoder–decoder U-shaped architecture to complete feature encoding, reconstruction and multi-scale feature extraction, which effectively captures the inherent periodic and seasonal patterns of wind speed. Experiments on the SDWPF wind farm dataset show that U-STNet obtains competitive prediction accuracy across all multi-step forecasting horizons. Compared with traditional statistical models, recurrent neural networks and state-of-the-art Transformer baselines, the proposed method exhibits more stable error accumulation characteristics and superior long-step prediction performance. This verifies the effectiveness of jointly modeling turbine spatial topology and multi-scale temporal dependencies for wind speed forecasting. Full article

Vibrio parahaemolyticus is a leading cause of seafood-associated gastroenteritis, yet the ecological and genomic significance of environmental populations as potential reservoirs remains incompletely characterized. From May to October 2024, 108 water samples were collected monthly at one coastal and two estuarine sites in Haiyan, Zhejiang. Confirmed isolates (n = 39) underwent whole-genome sequencing, MLST/cgMLST, virulence and AMR gene screening, integron characterization with BLASTp (+2.17.0) integrase family typing, and viral-region prediction. Culture-based detection was absent in May and rose to 66.7% in October (Cochran–Armitage trend, p = 0.003), with descriptively higher detection at the coastal site. MLST identified 28 STs including four novel types (Simpson’s diversity = 0.953). All isolates lacked tdh, trh, and T3SS2 but retained T3SS1 and MAM7. One estuarine isolate carried CALIN-associated dfrA31 and qnrVC5. All integrases matched VpaIntIA (95.9–100% identity), not mobile class 1–3 integrases. Viral regions were detected in 38/39 isolates; filamentous phage annotations in 52.6%. Haiyan coastal V. parahaemolyticus shows seasonal and spatial patterns, high diversity, and a mobilome dominated by chromosomal super-integrons, underscoring the need for integrase family typing in environmental surveillance. Full article

Electrochemical corrosion is one of the main causes of degradation of substation grounding devices and directly impacts the operational reliability of electric power facilities. Despite numerous studies on individual corrosion factors, comprehensive models considering the combined effects of soil physical and chemical properties and electrical operating conditions remain limited. This study analyzes emergency situations associated with grounding system failures and examines the effect of the main factors of electrochemical corrosion, including chloride ion concentration, soil moisture, environmental acidity, seasonal temperature changes, and leakage currents. Based on Faraday’s law, a mathematical model of electrochemical corrosion rate is proposed that combines the influence of the factors considered through correction factors. For practical implementation, an algorithm for the dynamic assessment of degradation of grounding system elements has been developed. The proposed model predicts changes in the cross-sectional area of grounding device elements, changes in grounding resistance, and the occurrence of potentially hazardous operating conditions. The developed algorithm assesses the risk of exceeding the permissible grounding potential, violating thermal withstand, the occurrence of hazardous step voltages, insulating breakdown, and disrupting the selectivity of relay protection devices. Full article

With the increasing frequency of extreme-heat events, there is an urgent need to identify the key drivers of the urban thermal environment at fine spatial scales. Focusing on urban blocks within Beijing’s Fifth Ring Road, this study integrates Landsat 8 imagery acquired from March 2020 to February 2021 with multi-source data on land cover, buildings, population, pollution, and topography. LightGBM–SHAP, a theory-informed directed acyclic graph (DAG), CausalForestDML, and cross-fitted g-computation were employed to investigate predictive associations, Q25–Q75 average total treatment effects, and block-level responses to prespecified urban-morphology intervention scenarios for seasonal land surface temperature (LST). The within-season SHAP analyses consistently placed building height (BH) and building density (BD) among the relatively important predictors, whereas the predictive patterns of the normalized difference vegetation index (NDVI) and the proportion of impervious surfaces (ID) were more season-specific. The autumn and winter models also assigned relatively high within-season importance to the digital elevation model (DEM), PM_2.5, and CO₂ emission proxy. Because the four seasonal models differed in predictive performance and LST distributions, these cross-seasonal patterns were interpreted qualitatively rather than as direct comparisons of absolute SHAP values or rank positions. Causal-effect estimation further indicated that, under the primary DAG and identification assumptions, the Q25–Q75 point estimates were positive for BD and negative for BH in all four seasons. In summer, the Q25–Q75 effects of NDVI and ID were −0.772 and +1.548 °C, respectively. Intervention-scenario analysis further indicated that the estimated responses varied across blocks and seasons, emphasizing the importance of considering baseline urban conditions and common support when interpreting potential planning effects. Additional spatially blocked validation yielded lower predictive performance than random validation, while significant positive residual spatial autocorrelation remained in all four seasons. These findings may inform the local evaluation of season- and context-specific surface-temperature mitigation strategies within the observed-support range, but they should not be interpreted as universal planning prescriptions. Full article

Water resources are essential for sustainable agricultural development, and accurate crop water requirement prediction is important for improving irrigation efficiency and optimizing water allocation in irrigation districts. This study focused on the Chushandian Irrigation District in Henan Province, China. Reference evapotranspiration (ET₀) was calculated using the FAO Penman–Monteith equation, and the monthly crop water requirements (ET_C) of wheat, peanut, rapeseed, corn, rice, and vegetables were estimated using crop coefficients (K_c). XGBoost feature importance, Pearson correlation, Mantel, and SHAP analyses were used to examine the meteorological drivers of crop water requirement. Atmospheric pressure showed high nonlinear predictive importance, whereas mean air temperature, relative humidity, and sunshine duration exhibited more consistent physical and statistical relationships with crop water requirement. A process-informed TCN–Transformer framework was then developed for joint and crop-specific prediction. The TCN module extracted local temporal variations, while the Transformer module captured long-term dependencies. In the joint prediction task, the proposed model achieved an R² of 0.9487 and an RMSE of 33.24 mm, outperforming the LSTM, GRU, and CNN–LSTM baselines. The crop-specific results further demonstrated that the model effectively represented seasonal variations and periods of relatively high water requirement across the six crops. The proposed framework can support monthly water-allocation planning and seasonal irrigation scheduling in multi-cropping irrigation districts. Full article

Feature engineering has become a common step in AI-based HVAC forecasting, often involving variables calculated from raw building management system (BMS) measurements, such as temperature differences, setpoint tracking deviations, airflow balance indicators, rolling statistics, and temporal or seasonal descriptors. Accurate short-term forecasting can provide a baseline of expected operation for anomaly and fault detection and can support control optimization and operator decision making. However, real-world deployment is complicated due to differences in BMS sensor availability and data quality, as well as the preprocessing and maintenance burden associated with complex feature sets. The actual contribution of these features to the performance of AI forecasting remains underexplored, particularly for short-term prediction of air handling unit (AHU) operation. This study evaluates the impact of features on short-term AHU forecasting using three deep learning (DL) architectures: Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN–LSTM model. An actual operational AHU dataset from a BMS was used to predict key operational variables, including supply and extract air temperatures, supply and extract fan operating signals, and supply air temperature setpoint-tracking error. Fan signal balance was additionally evaluated as a derived indicator calculated from the two predicted fan signals. Four input configurations were evaluated: (i) full (74 inputs), containing raw BMS measurements, short-cycle temporal variables, engineered and dynamic features, and annual-calendar information; (ii) no annual calendar (68 inputs), identical to full but excluding annual-calendar variables; (iii) raw + short-cycle temporal (20 inputs); and (iv) raw-only (12 inputs). The models used a 60-min input history to forecast the following 30-min at one-minute resolution. Persistence and Ridge models were included as reference baselines. All models were trained and tested on identical data splits and forecasting horizons to ensure a fair comparison. Each DL experiment was repeated across five independent runs, and performance was evaluated using MAE, RMSE, and R². The TCN showed the strongest overall DL performance. Raw-only achieved the highest mean R² in 11 of 15 architecture–target comparisons using just 12 inputs. The best mean DL R² ranged from 0.916 for the fan signals to 0.993 for extract air temperature. Annual-calendar features improved the TCN results but provided no consistent benefit for the LSTM or CNN–LSTM. Ridge slightly outperformed the best DL configurations for temperature-related targets, reflecting the strong short-term continuity of these signals. These findings show that recent raw BMS measurements contain most of the information needed for accurate 30-min AHU forecasting, while explicit seasonal and engineered features provide limited additional value. The resulting simpler models may in the future be used as forecasting components in predictive control and fault detection systems. However, their control and energy-saving benefits must be tested separately. Full article

The geosciences have witnessed a rapid expansion of analytical methods, from classical linear models to geostatistics and modern machine learning. However, no single resource compares a curated selection of these methods systematically while also providing reproducible code. This reproducibility-driven review evaluates seven analytical techniques applied to three simulated geoscience datasets: spatial points, time series, and a spatio-temporal grid. Methods were selected based on their availability in R to ensure transparency and accessibility. This paper serves as a historical review, a comparative benchmarking study, and a practical teaching resource with fully reproducible R code. The methods include linear regression, ARIMAX, ordinary kriging, regression-kriging, random forest, feed-forward neural networks, and BART. For spatial prediction, when evaluated against the true field, linear regression achieved the lowest mean RMSE (1.94 ± 0.23), followed by random forest (2.13 ± 0.23) and ordinary kriging (2.20 ± 0.26)—highlighting the importance of consistent out-of-sample validation. Regression-kriging performed similarly to ordinary kriging (2.22 ± 0.26). For time series, a feed-forward neural network (0.37 ± 0.09) substantially outperformed seasonal ARIMAX (1.90 ± 1.30). For spatio-temporal prediction, random forest and BART performed indistinguishably (0.534 ± 0.004 vs. 0.543 ± 0.004). A practical decision guide, grounded in these empirical results, summarises method selection based on sample size, data type, and research goal—whether inference, prediction, or uncertainty quantification. All code is open and reproducible, providing a template for future method comparisons. Full article

Future climate projections suggest an increase in heat-related mortality and a decrease in cold-related deaths under warming scenarios. Understanding the health impacts of extreme heat, and their implications for healthcare demand is essential for assessing the future burden of climate-related illnesses. In this study, we examined the relationship between extreme heat events and Emergency Department Visits (EDV) for heat-related illnesses (HRIs) across the United States from 2018 to 2025. Using data from the Centers for Disease Control and Prevention (CDC) Heat and Health Tracker and other relevant sources, we analyzed EDV rates standardized to 100,000 population. We aggregated daily into the 10 U.S. Health and Human Services (HHS) Regions. We used 0.5° × 0.5° gridded maximum daily temperature data (aggregated to HHS regions with proportional area weighting) and daily maximum heat index extracted from the CDC data portal (estimated using the US National Weather Service methodology and aggregated to HHS regions using total population weighting) to characterize seasonal patterns and regional variability. The association between peak heat events and EDV time series was explored using log-linear mixed-effects models, which accounted for seasonal trends, climate variables, and their regional variability. Random effects were used to capture regional heterogeneity in predictor-response relationships, accommodating variation in associations across regions. Model performance was evaluated using prediction error metrics and goodness-of-fit assessments. Maximum temperature and heat index were both significant predictors, with the heat index offering a slightly better fit. Associations were largely contemporaneous, with peak correlations at lag zero, underscoring the need for real-time response. EDV increased several days before peak environmental conditions, consistent with early exposure effects. While temperature-EDV relationships varied regionally, heat index associations were more stable. This work underscores the urgent need for regionally adaptive public health strategies in the face of intensifying climate extremes and outlines future directions for research and policy to strengthen health systems’ preparedness in a warming world. Full article

Reliable modelling of monthly air temperature is relevant to station-scale climate assessment and the evaluation of meteorological data-driven models. However, station-scale monthly meteorological datasets often contain correlated and partially redundant predictors because thermal, moisture, precipitation, wind, and seasonal variables are jointly controlled by atmospheric and seasonal forcing. This study conducts an integrated comparative analysis of established regression and machine learning models for monthly mean air temperature modelling in Zonguldak, a humid coastal province in the Western Black Sea Region of Türkiye. Monthly meteorological observations from 2000 to 2022 were used to evaluate eight primary regression and machine-learning models: Partial Least Squares regression, Ridge, Lasso, ElasticNet, Support Vector Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. Ordinary Least Squares (OLS) and Huber regression were additionally included as reference models. The analysis retained the original meteorological predictors and jointly evaluated predictive accuracy, model stability, ablation sensitivity, and model-specific predictor relevance. Reduced-predictor and seasonality-only scenarios were examined to distinguish direct thermal reconstruction from broader climatological predictability. Model performance was assessed using repeated nested cross-validation, bootstrap summaries of performance variability, supplementary rolling-origin validation, and Wilcoxon signed-rank tests with Holm correction. Although the full-predictor models achieved high predictive accuracy, this performance largely reflected the direct thermal information contained in minimum and maximum air temperature. When these thermal predictors were excluded, MAE increased to approximately 1.13–1.22 °C and $R^2$ decreased to approximately 0.93–0.94. The seasonality-only scenario yielded MAE values of approximately 1.27–1.34 °C and R² values of approximately 0.92, indicating that the annual cycle accounted for a substantial proportion of monthly temperature predictability. The additional non-thermal meteorological predictors provided only limited improvement beyond the strong seasonal baseline. Overall, model performance depended on the predictor information available, and no single model family showed a consistent advantage across the evaluated scenarios. These findings highlight the importance of considering predictive accuracy together with model stability and predictor dependence in data-limited station-scale temperature modelling. Full article

This study investigated how detection distance affects spectral models for soluble solids content (SSC) and firmness evaluation in Korla fragrant pears and provides a reference for calibrating standardized indoor non-destructive detection equipment. Two hundred visually intact fruit samples at the early-ripening stage were collected from the Korla production area in Xinjiang. An FS-640 multispectral camera system equipped with a VS-SWR fixed-focus industrial lens (16 mm focal length, F1.8 maximum aperture, 1/2-inch sensor format) was used to acquire fruit reflectance spectra at seven vertical lens-to-fruit-surface distances of 90, 100, 110, 120, 130, 140, and 150 cm. A 625-pixel region of interest (ROI) was selected using ENVI at an undamaged equatorial or near-equatorial position of each fruit, and the regional mean spectrum was used as the spectral feature of one fruit sample. The sample-set partitioning based on joint X–Y distances (SPXY) algorithm was used to divide the calibration and prediction sets at a 3:1 ratio after outlier removal via a residual-threshold method. Four preprocessing methods, namely LOESS smoothing, standardization, vector normalization, and Savitzky–Golay (SG) smoothing, were compared. Competitive adaptive reweighted sampling (CARS) was performed with 50 Monte-Carlo sampling runs, a maximum of 30 principal components, and 10-fold cross-validation, yielding 99 characteristic wavelengths. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were then established using identical input variables and sample partitions. Model performance was evaluated using the coefficient of determination for calibration ($R_c^2$), coefficient of determination for prediction ($R_P^2$), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), relative prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). Under the static laboratory acquisition conditions in this work, the SSC model achieved the best prediction performance at 110 cm with SG smoothing ($R_P^2$) = 0.8949, RPD = 3.0633, RPIQ = 5.8661), whereas the firmness model obtained optimal prediction performance at 140 cm with standardization ($R_P^2$) = 0.7460, RPD = 1.9425, RPIQ = 3.2867). Changes in detection distance altered illumination uniformity, effective reflected signal, photon-scattering paths, and background-noise proportion. These effects may partially explain why the chemical-absorption-dominated SSC index and the tissue-scattering-dominated firmness index responded differently to detection distance. The results provide a reference for setting spectral detection parameters for Korla fragrant pears; however, samples were obtained from only a single producing region, harvest season, and maturity stage, and no independent external validation dataset was used. Therefore, the generalization ability of the developed models needs to be further verified using cross-season and cross-orchard sample sets. Full article