Unmanned Aerial Vehicles (UAVs) offer enhanced spatial and temporal resolution for agricultural remote sensing, surpassing traditional satellite-based methods. Given the abundance of evolving machine-learning methods for crop recognition, this study evaluates and compares five machine learning algorithms (ML) and tests an Ensemble Learning method as a sixth approach, integrated with object-based image analysis (OBIA) for crop-type classification using UAV multispectral imagery, aiming to identify the most effective model and produce a classification map based on the best-performing method. Image segmentation was built using eCognition software, and spectral, index, and gray level co-occurrence matrix (GLCM) features were extracted from the segmented object. A machine learning model integrating multiple classification algorithms (SVM, ANN, RF, XGBoost, KNN, Ensemble Learning) with automated hyperparameter optimization was developed and executed in Google Colab using Python 3.10. All classifiers achieved accuracies exceeding 80% and Area Under the Curve (AUC) values above 0.9. SVM and ANN are the best classifiers, with the same value of accuracy (94%), followed by XGBoost (93%), RF (92%), and KNN (89%). The Ensemble Learning method (SVM + ANN) as a sixth approach outperformed all single models, with an accuracy value of 95%. Cotton, maize, peanut, and soybean were classified with the highest accuracy, with index and GLCM features contributing most significantly, followed by spectral features. The integration of high-resolution UAV imagery with ML and OBIA demonstrates strong potential for automated crop-type classification, offering valuable support for precision agriculture applications.

Real-time monitoring of cotton above-ground biomass (AGB) is crucial for monitoring crop growth and optimizing management practices. This study evaluated UAV-based RGB and multispectral (MS) imagery for cotton AGB estimation across multiple growth stages under different planting densities and sowing dates in Anyang, China. Spectral features, vegetation indices (VIs), and Gray Level Co-occurrence Matrix (GLCM) texture metrics were extracted and organized into three scenarios: RGB + MS, RGB-only, and MS-only. Recursive feature elimination with cross-validation (RFECV) was applied for feature selection, and six machine learning models were evaluated using both baseline and selected feature sets. Results showed that model performance was strongly influenced by growth stage, sensor configuration, and feature composition. Accuracy was highest at the seedling and squaring stages and decreased at flowering due to canopy complexity and spectral saturation. MS-only and fused features generally performed best at the seedling stage, while RGB-only features were competitive or superior at the squaring stage, highlighting the importance of high-resolution structural information. At flowering, fused RGB–MS features provided the most stable performance, although improvements were limited. RFECV exhibited stage-dependent behavior, improving performance mainly at early growth stages but showing inconsistent benefits later. SHAP analysis revealed a shift from texture-dominated predictors at the seedling stage to balanced feature contributions at squaring and vegetation index (VIs) dominance at flowering. Overall, cotton AGB estimation is a stage-dependent process requiring adaptive sensor and feature selection strategies.

Microbial carbon use efficiency (CUE) is believed to be a key regulator of the accumulation of persistent soil organic carbon. Microbial processing of organic carbon in soil occurs in the pore network, the properties of which are known to affect microbial activity. However, the effects of interactions between pore architecture, and microbial life-history strategies on carbon dynamics, and house these interactions are modulated by moisture regime, remain poorly understood. Here, we use a spatially explicit model based on a cellular automaton, applied to realistic soil pore networks derived from X-ray tomographic images, to investigate how pore geometry and organic matter distribution shape microbial dynamics and CUE. The model explicitly represents r- and K-strategists that are either motile or immobile, and have different resource requirements for growth, maintenance and motility. The constraints imposed on substrate diffusion and the spatial domain in which motile bacteria can effectively disperse by the moisture regime are explicitly represented in the model, in order to capture how moisture regulate decomposer access to resources. The simulations show that pore architecture has a significant effect on microbial CUE. Micropores promoted high CUE and a dominance of K-strategists by limiting substrate diffusion, reducing pore connectivity, and constraining microbial access to spatially isolated resources. In contrast, macropores with high connectivity and greater substrate accessibility favor r-strategists, rapid biomass turnover and lower CUE. Motility emerges as advantageous in connected, carbon-rich macropores, but energetically unfavourable in confined or poorly connected micropores, where immobile strategies outperform motile ones. Overall, our results indicate that microbial CUE is not an intrinsic property of life-strategies alone, but emerges from the interaction between microbial life-strategies and pore-scale physical constraints. This study suggests that soil structure influences microbial strategies by limiting resource availability and dispersal.

Microbial carbon use efficiency (CUE) is believed to be a key regulator of the accumulation of persistent soil organic carbon. Microbial processing of organic carbon in soil occurs in the pore network, the properties of which are known to affect microbial activity. However, the effects of interactions between pore architecture and microbial life-history strategies on carbon dynamics, and how these interactions are modulated by moisture conditions, remain poorly understood. Here, we use a spatially explicit model based on a cellular automaton, applied to realistic soil pore networks derived from X-ray tomographic images, to investigate how pore geometry and organic matter distribution shape microbial dynamics and CUE. The model explicitly represents r- and K-strategists that are either motile or immobile, and have different resource requirements for growth, maintenance and motility. The constraints imposed by the moisture conditions on substrate diffusion and the spatial domain in which motile bacteria can effectively disperse are explicitly represented in the model, in order to capture how moisture regulates decomposer access to resources. The simulations show that pore architecture has a significant effect on microbial CUE. Micropores promoted high CUE and a dominance of K-strategists by limiting substrate diffusion, reducing pore connectivity, and constraining microbial access to spatially isolated resources. In contrast, macropores with high connectivity and greater substrate accessibility favour r-strategists, rapid biomass turnover and lower CUE. Motility emerges as advantageous in connected, carbon-rich macropores, but energetically unfavourable in confined or poorly connected micropores, where immobile strategies outperform motile ones. Overall, our results indicate that microbial CUE is not an intrinsic property of life-strategies alone, but emerges from the interaction between microbial life-strategies and pore-scale physical constraints. This study suggests that soil structure influences microbial strategies by limiting resource availability and dispersal.