Anaerobic fermentation converts crop residues, livestock manure, and other agricultural wastes into energy-rich products, particularly methane and hydrogen, simultaneously enabling waste treatment and resource recovery. However, stable operation and industrial scale-up remain difficult because feedstock properties, microbial metabolism, fluid behavior, and mass transfer vary continuously throughout fermentation. Their interactions generate nonlinear, time-varying, and multiscale dynamics that are difficult to observe, predict, and regulate using conventional methods. This review synthesizes progress in modeling and intelligent control for the anaerobic fermentation of agricultural wastes, with particular attention to biochemical hydrogen production. The reviewed advances are organized around a hierarchical data-mechanism-control framework that connects process characterization, hybrid modeling, online estimation, optimization, and system-level decision support. First, the review examines heterogeneous data generated during feedstock characterization, pretreatment, reactor operation, microbial conversion, and product formation. Laboratory and pilot-scale datasets are often sparse, noisy, imbalanced, and dependent on specific feedstocks or reactor configurations. Second, the review compares major modeling strategies and clarifies their respective roles. Random forests and support vector machines are effective for modeling feedstock characteristics and pretreatment responses, whereas artificial neural networks and long short-term memory networks are capable of capturing nonlinear relationships and temporal dependencies. Gaussian process regression supports uncertainty analysis under limited data, while transfer learning can reuse information across related feedstocks or operating scenarios. Despite these capabilities, purely data-driven models may generate unreliable predictions outside their training domains and may violate conservation laws, thermodynamic constraints, or microbial kinetics. Mechanistic models, including anaerobic digestion model No. 1 (ADM1), provide interpretable process descriptions but require extensive calibration and may inadequately represent complex slurry transport and microbial succession. Physics-informed neural networks (PINNs) provide a gray-box modeling approach by embedding conservation relationships and microbial kinetic equations into the learning objective. Explainable artificial intelligence (XAI), particularly SHapley Additive exPlanations (SHAP), complements this approach by identifying influential variables, interactions, inhibitory conditions, and potential process thresholds. These tools can translate model outputs into operationally meaningful evidence for process diagnosis and control, although their reliability under practical operating conditions remains to be validated in reactors treating agricultural wastes. Third, the review links hybrid modeling to closed-loop process control. Internet of things (IoT) sensor networks and edge computing can support local filtering, drift correction, feature extraction, communication management, and rapid fault detection. Soft sensors can estimate difficult-to-measure state variables, including volatile fatty acids, chemical oxygen demand, alkalinity, and microbial activity, from readily available online measurements. Multi-objective optimization and model predictive control (MPC) can then balance gas productivity, process stability, and energy consumption while satisfying equipment and safety constraints. At the system level, digital twins integrate biochemical kinetics, computational fluid dynamics, sensor data, equipment models, and scenario simulation. Nevertheless, applications in agricultural waste fermentation remain largely conceptual or limited to individual components because biological and hydrodynamic processes are difficult to couple efficiently. Future research should prioritize standardized multimodal databases, shared biochemical and hydrodynamic model libraries, adaptive hybrid models, robust sensing, uncertainty-aware control, and integrated device-edge-cloud architectures. Progress in these areas can advance interpretable prediction, resilient process operation, and reliable bioenergy recovery, thereby supporting the digital and intelligent transformation of agricultural waste management systems.

Non-grain cultivation on arable land has posed an ever increasing challenge on national food security in sustainable agriculture. Particularly, the winter wheat–summer maize rotation systems dominate cereal production in the North China Plain. It is therefore required to accurately identify and constantly monitor the crop rotation patterns for the land-use transitions. However, existing remote sensing approaches remain dependent largely on single-temporal imagery during optimal phenological windows. Cloud contamination can frequently cause to capture the continuous and nonlinear dynamics of multi-season cropping systems. In this study, a time-series framework was developed to identify the crop rotation patterns from dynamic monitoring data. Continuous Change Detection and Classification (CCDC) algorithm was integrated with machine learning models using dense Sentinel-2 observations. A case study was also selected as the Hua County, Henan Province, China. The representative wheat–maize double-cropping region was characterized by spatial heterogeneity. All available Sentinel-2 Level-2A images were acquired from 2018 to 2024, and then processed on the Google Earth Engine (GEE) platform. The continuous multi-year spectral time series were constructed after image processing. A feature space was designed to characterize the crop phenology and surface conditions. Multi-spectral reflectance bands were incorporated with the six vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Yellow Index, Normalized Difference Red Edge Index (NDREI), and Inverted Red-Edge Chlorophyll Index (IRECI). A systematic comparison was made on two classifications. One was an improved CCDC algorithm. The third-order harmonic regression was fitted into the pixel-level time series to explicitly capture intra-annual growth rhythms and long-term trends. Another was a conventional single-phase approach. Median composites were generated for the key phenological periods of wheat and maize. Annual rotation patterns were then inferred after seasonal overlay. Subsequently, the regression coefficients, harmonic components, amplitudes, and phase parameters from the CCDC model were input for Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classifiers. The performance was evaluated using five-fold cross-validation and multiple accuracy metrics. The results demonstrate that the CCDC framework was achieved in the high accuracy, robustness and consistency to classify annual rotation patterns in the continuous crop transitions under variable atmospheric conditions. Among them, the highest performance was found in the CCDC–ANN combination, with an average overall accuracy of 91.8% and a Kappa coefficient of 0.891, which was improved by approximately 20% over the conventional approach. The superior performance of the ANN model was also obtained to learn complex nonlinear relationships in dense time-series features. Spatiotemporal analysis further revealed the substantial heterogeneity in the crop rotation patterns. Staple–non-staple rotations were concentrated in western hilly areas with the complex terrain, whereas the stable staple–staple and non-staple–staple systems were dominated in the eastern plains. Temporally, all major rotation types exhibited an “increase–decline–recovery” trajectory from 2018 to 2024. The great variation was primarily attributed to the policy adjustments, market dynamics, and environmental constraints. Overall, the CCDC–ANN framework can provide an accurate and scalable solution to map the wheat–maize rotation systems. The finding can also offer the strong potential to the regional monitoring of non-grain cropland and land use in modern agriculture.