Agricultural carbon emissions have been generated by human activities in vast regions. It is often required to accurately understand the status, spatiotemporal patterns, and future trends of agricultural carbon emissions. It is also crucial to optimize carbon sequestration and emission reduction against climate adaptation. However, current assessments can rely heavily on statistical data, where regions with incomplete statistical records can introduce great uncertainties in carbon accounting and forecasting. Taking Chongqing as a case study, a systematic investigation was conducted to explore the spatiotemporal patterns and future trends of agricultural carbon emissions from 2004 to 2023. Multi-source agricultural data was also combined with statistics and remote sensing monitoring at the county level. Furthermore, spatiotemporal analysis was employed to examine the evolution, including slope estimation, the Mann-Kendall test, Moran's I index, and the Getis-Ord Gi* index. While the prediction models were then constructed for the trends, such as ARIMA and three machine learning methods (support vector machine, random forest, and XGBoost). The results indicate that: 1) The feasible and reliable performance was achieved to evaluate agricultural carbon emission using multi-source data, particularly with the average annual agricultural carbon emission of 2.435 million tons. There was a significant correlation with the conventional statistical data (R²=0.932, P<0.001), thus compensating for missing county-level statistical data. The higher stability was also achieved after evaluation. 2) There were significant source and regional differences in agricultural carbon emissions. The primary sources were methane emissions from rice cultivation and carbon emissions from fertilizer use, with average annual emissions of 1.175 million and 0.809 million tons, respectively. The spatial agglomeration of agricultural carbon emissions was intensified year by year, with the global Moran's I index of 0.695, 0.615, and 0.64 in 2017, 2021, and 2023, respectively. Specifically, Wanzhou, Liangping, and Zhongxian were identified as emission hotspots, with average annual agricultural carbon emissions of 0.106 million, 0.105 million, and 0.089 million tons, respectively; Whereas Nan'an, Jiulongpo, and Beibei were identified as emission cold spots, with average annual emissions of 6.996 thousand, 15.694 thousand, and 29.679 thousand tons, respectively. 3) The interpretable ARIMA-XGBoost prediction model performed well on an independent test set (R²=0.936). The agricultural carbon emissions were shifted from a generally stable state to a more widespread downward trend. Total emissions were projected to gradually decrease from 2.187 million to 1.788 million tons between 2024 and 2030. More significant influencing factors were determined as the rural employees, highway mileage, and gross product in agricultural carbon emissions. Yet there was no variation in the spatially differentiated distribution over counties. Multi-source data can offer information complementarity and reliability to assess regional agricultural carbon emissions. The findings can provide a scientific foundation for low-carbon sequestration and emission reduction. A valuable reference can also serve as the low carbon strategies in similar regions.

Quantitative evaluation standards are often required to accurately predict sprouting regeneration, particularly for cutting quality. However, the coppicing surface targets cannot be recognized in complex fields during Caragana korshinskii shrub coppicing in the arid regions of Northwest China. In this study, a quantitative evaluation was proposed for sprouting regeneration, according to the synergistic association between cutting morphologies and agronomic traits. Accurate recognition of coppicing surfaces was achieved in unstructured field environments. A segmentation network was constructed, named WoodGrainNet. Firstly, the global context modelling and target localization were enhanced via a semantic stream using a lightweight Transformer architecture. Secondly, a frequency-domain stream was incorporated with the Haar discrete wavelet transform to effectively suppress abiotic background interference for the texture representation of the targets. Simultaneously, a shape stream was combined with a differentiable Sobel operator. Explicit constraints were also applied to high-frequency gradient regions. Thereby, the adjacent cut boundaries were delineated to effectively alleviate the adhesion of dense targets. Three-stream features were synergistically simulated for the deep integration. The WoodGrainNet significantly improved the segmentation robustness and instance discrimination of the improved model in complex scenes, particularly at the level of underlying physical feature representation. Accurate segmentation was achieved to automatically extract a key geometric phenotypic indicator for coppicing quality—coppicing surface circularity (C). Accordingly, the coppicing quality grading and sprouting prediction were established after evaluation. The experimental results demonstrate that the better performance of WoodGrainNet was achieved to balance high accuracy and real-time processing, with a mean Intersection over Union (mIoU) of 86.99% and an inference speed of 51.78 frames/s. The performance was significantly superior to mainstream networks, such as DeepLabV3+, effectively recognizing tiny coppicing targets. In terms of agronomic analysis, statistical results revealed that there was a significant positive correlation between the morphological quality of the coppicing surface and the sprouting potential of lateral branches. Notably, the coefficient of determination (R²) between the coppicing surface circularity (C) and the number of lateral branch sprouts reached 0.764 at the 5th week (N). The circularity served as an important morphological indicator to evaluate coppicing quality and sprouting regeneration. Field tests were conducted to verify its feasibility for engineering applications. The improved model was deployed on a mobile intelligent system. A discrimination accuracy of 90.8% was achieved for coppicing quality grades under mobile working conditions. Sprouting prediction trend was highly consistent with the measured ones, indicating its potential to replace manual inspection and subjective empirical judgment. The pixel-level phenotypic analysis of the coppicing surface was effectively transformed to predict the single-plant regeneration. The finding can provide a theoretical basis and technical support for the quality evaluation, parameter monitoring, and precise detection of ecological shrub forests in arid regions.

Conventional processing cannot fully modulate multiple biological activities of bioactive Polygonatum sibiricum polysaccharides using structural modifications and complex conformational transformations. The present study aims to clarify the different effects of steaming and fermentation—two representative processing techniques, including dynamic structural evolution of the medicinally valuable polysaccharides and the subsequent precise regulation of their bioactivities. Polysaccharides were extracted and then purified from fresh Polygonatum sibiricum, conventionally three-times-steamed Polygonatum sibiricum, and steamed-then-fermented Polygonatum sibiricum. The polysaccharide samples were designated as FPP, PPP, and FMP, respectively. An analytical platform was also employed: high-performance gel permeation chromatography (HPGPC) was used to determine molecular weight distributions; high-performance liquid chromatography (HPLC) with pre-column derivatization was used to quantify monosaccharide composition; Fourier transform infrared spectroscopy (FT-IR) was used to identify characteristic functional groups and conformational transitions. Zeta potential and dynamic light scattering analysis were used to assess colloidal stability and particle size uniformity, while scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to visualize morphological and nanostructure transformations, respectively. Antioxidant and hypoglycemic activities were evaluated through in vitro assays. Specifically, the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacities were determined with α-amylase and α-glucosidase inhibitory kinetics. Steaming was used to alter the structural integrity of the polysaccharides, disrupt their native triple-helix conformation, modify the monosaccharide profile, shift molecular weight distribution toward higher ranges, and dramatically increase uronic acid content. Subsequently, fermentation acted as the precise biological modification, further fine-tuning monosaccharide compositional ratios using microbial enzymatic hydrolysis. Biotransformation raised the absolute Zeta potential, electrostatic repulsion and colloidal stability, and particle size distribution, indicating the remarkable homogeneity. These conformations were visually captured by microstructural observations. In SEM imagery, morphological transitions were observed: FPP displayed a smooth, continuous sheet-like film; Steaming induced regularly arranged, protrusive structures; Fermentation generated a porous network morphology, thereby increasing structural porosity and specific surface area. According to these morphological shifts in AFM images, nanoscale transformations occurred from flexible, worm-like chains of FPP to compact, spherical chains in both PPP and FMP. The average chain height increased markedly after steaming and then decreased after fermentation. Functionally, these structural modifications were closely correlated with enhanced bioactivities. In antioxidant evaluations, PPP exhibited the most potent DPPH radical scavenging capacity. Meanwhile, FMP presented robust ABTS radical scavenging activity compared with the PPP. In hypoglycemic potential, both PPP and FMP were more effectively inhibited α-amylase than native FPP. Crucially, FMP shared the optimal inhibitory effect against α-glucosidase among all tested samples. Steaming and fermentation served as effective strategies to modulate the chemical structure, spatial conformation, and microscopic morphology of Polygonatum sibiricum polysaccharides, thereby enhancing their antioxidant and hypoglycemic activities. The processing-induced structural modifications showed a strong correlation between specific structural features and physiological functions in plant polysaccharides. Consequently, this finding can also provide a solid theoretical foundation and practical guidance for the precise, high-value industrial application of Polygonatum sibiricum resources in the functional food using traditional Chinese medicine processing.

Accurate and rapid recognition is often required for the king oyster mushroom under dark and humid environments using machine vision. Particularly, the mushrooms can intertwine with mycelial networks after imaging. This study aims to accurately locate king oyster mushrooms under dark and damp environments. An identification framework of the king oyster mushroom was proposed using an infrared array. A test bench was established based on an infrared reflection module. Sensing distance and sensing space cross-sectional radius of the infrared reflection module were then measured under different electrical parameters. A numerical simulation was used to fit the relationship between electrical parameters and infrared sensing distances. A mathematical model was obtained for the infrared reflection under typical parameter conditions. The invisible infrared light was then visualized for mushroom identification, according to the sensing space model from the infrared reflection module. A detection gantry (including multiple linearly arranged infrared reflection modules) was placed over a tray. The tray moved at a constant speed. The output levels of the infrared reflection modules were sampled periodically by an STM32F103ZET6 controller. An information matrix was formed for the position and shape of the mushrooms within the tray area. The sensing information matrix was determined by the number of timed samplings and the infrared reflection modules. Simple matrix operations were performed on the single-connected regions with zero values in the sensing information matrix. According to the morphologies of the king oyster mushroom, a double-cylinder rotary harvester was designed to calculate the position coordinates of the mushroom's center point relative to the tray. A servo motor drove a reducer, thus causing two coaxially arranged hollow cylinders to rotate relative to each other. Six blades evenly distributed below the cylinders were used to cut the king oyster mushroom in a planar motion. The harvester was fixed to a vertically moving linear module. The motion axes were arranged on an execution gantry, thereby driving the horizontal and vertical movements of the harvester. A king oyster mushroom harvesting device was developed, where the detection and execution gantries were sequentially placed on the tray. Taking king oyster mushrooms as the targets, five symmetrically distributed feature positions on the tray were selected for the identification and harvesting experiments. A series of experiments was conducted to verify the device. The time to solve for the mushroom center point coordinates was 3.5-4.5 ms using the information matrix, which was significantly less than the 40-50 ms required for mushroom image processing using machine vision. The high efficiency of the identification was obtained using the infrared array. Meanwhile, the deviation between the inner hole of the harvester and the mushroom cap center was controlled within 1.5-4.0 mm after the harvester was positioned on the mushroom. This gap met the operational quality requirements of mushroom harvesting robots. The harvesting success rate reached 100%, indicating the high positioning accuracy of the motion axis. The king oyster mushroom identification was developed using typical infrared reflection modules, whose cost was only 1/20 of the mainstream machine vision products. The outstanding efficiency and economic advantages were achieved in the strongly interfering substrate and mycelial networks. The findings can also provide a technical pathway for the edible king oyster mushroom harvesting under complex backgrounds.

To address the feed arching phenomenon that occurs during the feeding process of beef cattle and to satisfy the individualized feeding requirements of beef cattle, a roller-brush type supplementary feeding and pushing robot was designed in this study, which consists of a roller-brush pushing device, a screw-type supplementary feeding device, and an Ackermann chassis. The structural design of the feeding screw and the pushing roller brush was completed, and the motion behavior of feed particles was analyzed. In order to investigate the influence of the motion parameters of the supplementary feeding and pushing robot on the feeding and pushing performance, a simulation analysis of the robot’s motion process was carried out based on the EDEM-RecurDyn coupling method. First, the contact parameters among total mixed ration (TMR) particles as well as between the feed and the mechanical components were determined. Subsequently, dynamic models of the supplementary feeding device and the pushing device were respectively constructed in RecurDyn, and a flexible mesh was generated for the roller brush. Finally, a feed particle model was built in the EDEM software, and the device models were imported to complete the coupled simulation. In the study of feeding performance, the screw rotation speed and the robot’s travelling speed were taken as experimental factors, while feeding uniformity and feeding efficiency were used as evaluation indicators. In the study of pushing performance, the roller brush rotation speed, the roller brush deflection angle, and the robot’s travelling speed were taken as experimental factors, and the pushing rate and pushing efficiency were used as evaluation indicators. Single-factor and orthogonal experimental methods were adopted for the simulation tests. The simulation results showed that when the screw rotation speed of the supplementary feeding and pushing robot was 160 r/min, the robot travelling speed was 0.68 m/s, the roller brush rotation speed was 450 r/min, and the roller brush deflection angle was 40°, the feeding uniformity exceeded 96%, the feeding efficiency reached 120.6 kg/min, the pushing rate was 98.25%, and the pushing efficiency was 418.94 kg/min. Prototype tests were carried out under these optimal parameters, and the obtained results were as follows: feeding uniformity greater than 93%, feeding efficiency of 135.8 kg/min, pushing rate of 97.90%, and pushing efficiency of 311.90 kg/min. The designed robot exhibits good working performance and can meet the auxiliary feeding requirements of small- and medium-scale cattle barns.

Suburban rural areas of metropolitan regions can serve as the interface between urban and rural elements. Their rural settlements have posed challenges in recent years, such as idle and inefficient land use, scattered spatial layouts, and supply-demand mismatches in functions. Rural settlement consolidation can be expected to increasingly emphasize rural revitalization and territory-wide land consolidation. An accurate release of consolidation potential is often required for the precise alignment between land supply and development demand. Therefore, this study aims to assess the potential of rural settlements for the differentiated consolidation pathways in rural stock resources. A case study was taken of Jurong City, a suburb of the Nanjing metropolitan area. A "supply-demand assessment-type identification” framework was constructed using supply-demand theory. Supply–demand consolidation potential of rural settlements was also assessed using a one-class support vector machine (SVM) and machine learning. Furthermore, the precise pathways of land resource allocation were also explored at the village scale, according to the rural dominant function demands. The results show: 1) The theoretical consolidation potential of rural settlements was 8 490.19 hm², accounting for 61.51% of the settlement area. The willingness simulation model was validated with an accuracy of 87.81% and a recall rate of 95.48%, indicating high precision. The modeled willingness values ranged from 0.33 to 0.76 for administrative villages. Specifically, the villages with higher consolidation willingness were distributed in the southeastern region, including the Maoshan Scenic Area, Maoshan, Houbai, and Tianwang Town. The actual consolidation potential of rural settlements was 5441.91 hm² after correction, representing 39.42% of the rural settlement land. 2) The demand potential of rural revitalization ranged from 0.20 to 0.60, indicating a pattern of “higher in the west, lower in the east, with clustered distribution.” The number of medium-demand villages was the largest among all villages, totaling 100 (53% of all villages). 3) Four types of zones were identified for the supply-demand potential: priority consolidation, reserve regulation, demand-oriented, and stable control zone, accounting for 43, 35, 73, and 37 villages, respectively. 4) Four types of dominant functional demands were identified among the rural settlements, with the majority of balanced development villages. Most villages shared no dominant functional demand, resulting in a balanced functional profile with scattered spatial distribution. Potential zones were coupled with dominant functional demands. 12 consolidation types were derived at the village scale. Potential release pathways were summarized, such as “spatial reconstruction,” “precise guidance,” “flexible regulation,” and “micro-renewal,” thus enabling “one village, one strategy” precise intervention. Targeted consolidation measures were also proposed, such as “releasing potential through village reorganization” and “innovating dynamic land reservation supply.” Differentiated strategies were implemented to promote intensive and economical land use for the rural functions and spatial patterns. The findings can also provide a practical demonstration and reference to precisely match land supply with rural revitalization demands in the spatial units of metropolitan suburbs.

Accurately adjusting the feeding intake is often required in pond aquaculture of Micropterus salmoides. In this study, a dual-model progressive feeding strategy was proposed to accurately forecast the postprandial feeding status of the subsequent round using a time series model. The decrement mechanism was first triggered. Subsequently, a classification model was utilized to determine the postprandial feeding status of the current round, enabling the termination of the decrement mechanism. The experimental system consisted of three pond culture tanks stocked with Micropterus salmoides, with initial body masses of (161.47±11.02) , (204.36±17.09) , and (220.25±22.78) g, and the stocking densities of 224, 301, and 294 individuals, respectively. Feeding experiments were conducted using a multi-round feeding protocol within a single session. Audio data was collected during feeding using the digital hydrophone, while video data was obtained using a camera. Meanwhile, multimodal data including light intensity, water temperature, body mass, per-round feeding rate, and population abundance were synchronously recorded for per-round feeding. The stabilization of the cumulative feeding energy curve was adopted to determine feeding termination. A dynamic adaptive threshold segmentation was employed to accurately identify the feeding audio within the collected audio. Principal component analysis was conducted on the feeding features to extract from the feeding audio. Feature selection was employed to identify sensitive features to feeding status, which served as the inputs for model training and classification. Four models 1D convolutional neural network, Long short-term memory, gated recurrent unit, and transformer were improved to construct the feeding prediction models with time series. The optimal model was selected to predict the postprandial feeding status of the subsequent round, serving as the first-stage model of feeding strategy. Eight models k-Nearest Neighbors, decision tree, support vector machine, random forests, adaBoost, gradient boosting decision trees, extreme gradient boosting, and light gradient boosting machine (LightGBM) were employed to construct feeding status classification models. The best classification performance was selected as the feeding determination model in the second stage. Results showed that the sensitive features included overall features, light intensity, water temperature, body mass, and per-round feeding rate. A classification between the original and sensitive features demonstrated the effectiveness of the sensitive features, according to gradient boosting decision trees and random forests models. Transformer-regression-classification (transformer-RC) model outperformed the rest of the models across 1 to 3-time windows, with the accuracy from 0.93 to 0.94. The Transformer-RC model effectively predicted the postprandial feeding status of the subsequent round. All five ensemble models achieved strong classification, which outperformed the three base models. Among them, the LightGBM model achieved an accuracy of 0.98 for inputs to the classification model of feeding status in the second stage of the feeding strategy. Both the Transformer-RC and LightGBM models shared high prediction and classification after validation, with average values of four evaluation metrics exceeding 0.89 under both feeding states, indicating strong generalization. This finding can provide a strong reference to develop intelligent feeding.

Surface litter manure can significantly increase the risk of diseases in broiler brooding houses, such as coccidiosis and colibacillosis. Indoor air quality can be deteriorated, due to the release of ammonia, hydrogen sulfide, and methane. However, existing double-crank shoveling-throwing mechanisms of surface manure cleaning have suffered from low efficiency, performance, and excessive vibration, because the key structural parameters are determined empirically without systematic multi-objective optimization. In this study, a multi-objective optimization was developed for the double-crank shoveling-throwing mechanism using an improved NSGA-II algorithm. Thereby, better performance was achieved to improve the shoveling efficiency, energy consumption, and stability. A kinematic and dynamic model of the double-crank shoveling-throwing mechanism was first established to reveal the influence of structural parameters on the shoveling trajectory and force behaviors. A coupled ADAMS-EDEM simulation model was then constructed to simulate the interaction between the mechanism and manure particles. As such, 60 sets of variable samples were generated after the optimal Latin hypercube sampling. A Gaussian process regression (GPR) surrogate model was constructed to map the relationship between six variables and the shovel throwing quality Q. The relative error between the prediction and simulation was 3.85%, indicating high prediction accuracy. An improved NSGA-II algorithm was proposed to overcome the limitations of the standard NSGA-II algorithm—namely, significant dimensional differences among the three objectives, highly nonlinear parameter-performance mapping, and discontinuous parameter space. Three improvements were introduced: (1) an adaptive normalization mechanism to eliminate dimensional effects; (2) a hybrid optimization framework with global search (NSGA-II) and local refinement (sequential quadratic programming, SQP) for the high convergence accuracy; and (3) an improved crowding distance and solution selection mechanism for the distribution uniformity of the Pareto front. The improved algorithm was compared with standard NSGA-II, NSGA-III, MOPSO, and MOEA/D, according to three performance indicators: Inverted Generational Distance (IGD), Spacing, and Hypervolume (HV). The results showed that the improved NSGA-II algorithm significantly outperformed the rest. Specifically, the IGD value decreased by 71%, 68%, and 64%, respectively, compared with standard NSGA-II, MOPSO, and MOEA/D. Spacing value decreased by 26%, compared with standard NSGA-II, whereas, the HV value increased by 16%. The better performance was achieved in the high convergence, more uniform distribution, and higher coverage of the true Pareto front. The optimal compromise solution was selected from the Pareto set using the entropy-weighted TOPSIS. The optimal parameters were recommended: l₁=70.4 mm, l₂=88.5 mm, l₃=100.2 mm, l₄=30.3 mm, b=99.7 mm, β=37.4°. A prototype was manufactured for the double-crank shoveling-throwing device, according to the optimal parameters. Field experiments were conducted in three repetitions in a brooding house in Zhouzhi County, Xi’an, Shaanxi Province, China, in December 2025. The experimental conditions were as follows: Litter layer with a thickness of 50–60 mm, chicken manure layer thickness of 10-20 mm, and manure moisture content of 30%–35%. The results demonstrated that the optimal mechanism improved shoveling efficiency by 138.24%, whereas the driving torque and the angular acceleration peak at the shovel end were reduced by 35.12%, and 35.60%, respectively. In addition, the residual rate of surface manure decreased from 18.45% to 8.13%, with a reduction of 55.99%, indicating significantly improved cleaning quality. A multi-objective optimization framework was provided for the double-crank shoveling-throwing mechanism using ADAMS-EDEM simulation, GPR surrogate modeling, and an improved NSGA-II algorithm. The dimensional differences and high nonlinearity were avoided for the low computational cost after engineering optimization. The improved NSGA-II algorithm demonstrated superior convergence and distribution performance, compared with mainstream multi-objective algorithms. The optimal mechanism was achieved to balance shoveling efficiency, energy consumption, and operational stability. The findings can offer a complete technical pathway to enhance the performance of hinge-type multi-bar mechanisms, particularly for manure cleaning equipment in the poultry industry.

Farmland shelterbelts can be represented as typical narrow linear features in remote sensing imagery. However, the significant challenges remain in accurate segmentation, due to their strong global contextual dependencies and weak local features. In this study, a semantic segmentation model, named MFF-Net, was proposed to accurately and rapidly extract cultivated land and shelterbelts. Multi-feature fusion block (MFFB) and spatially gated fusion mechanism (SGFM) were also adaptively integrated with three complementary approaches: the long-range dependency modeling using a mamba-like linear attention (MLLA) operator, the local detail perception in convolutional networks, and the frequency-domain edge enhancement via fast Fourier transform (FFT). The feature representation was effectively balanced when segmenting elongated objects. Furthermore, a task- oriented super-resolution preprocessing was introduced into the network. This front-end step was designed to reconstruct high-resolution images. Thereby, the textual details were enhanced to sharpen the boundaries of subtle features, like shelterbelts. Superior input was provided for the subsequent segmentation model. A series of experiments was performed on the self-constructed dataset of the farmland shelterbelt. The results demonstrate that the superior performance of MFF-Net was achieved in the precision rates of 96.42% for cropland and 82.83% for shelterbelts, with a mean intersection over union (mIoU) of 83.45%, thus outperforming a range of advanced models, including RS³Mamba, DC-Swin, DeepLabV3+, and SegMAN. Ablation studies validated the effectiveness of each component within the multi-feature fusion. Frequency-domain features via FFTFormer contributed to a 2.80% increase in the shelterbelt IoU, indicating the enhanced boundary discrimination. The spatially gated fusion mechanism (SGFM) further boosted the overall mIoU by 1.71%, indicating the adaptive balance on the contribution rates from different feature domains. Compared with a baseline model, the full MFFB was improved mIoU of 5.14%. Super-resolution preprocessing was integrated with the highly effective auxiliary strategy. Taking 4x upsampled images as the input, there was a remarkable 6.61% increase in shelterbelt IoU and a 2.47% gain in overall mIoU. The difficulties with segmenting narrow targets were effectively mitigated to augment their pixel-width and edge clarity. A complete technical pipeline was established from pixel-level segmentation to vectorization and application. Accurate area estimation was achieved with average relative errors of 7.50% for cropland and 6.76% for shelterbelts, compared with the national survey data. An application analysis of shelterbelt closure degree was also made on individual farmland plots. The 91.95% of the plots met the national standard requirement (≥0.75). In conclusion, the MFF-Net model can be expected to effectively segment the narrow linear features in complex landscapes. Synergistic fusion of global, local, and frequency-domain features was combined with task-oriented super-resolution enhancement. This research can provide a robust technical pathway for the precise monitoring and dynamic evaluation of farmland shelterbelt networks in ecological conservation.

Due to the poor mass transfer, long reaction time, and severe material back mixing in conventional stirring reactions, the microfluidic reaction system, with the advantages of enhanced mass transfer, fast reaction speed, and mitigated substrate inhibition, has received attention. Natural wood is a cheap, renewable, and earth-abundant material, which is regarded as the ideal model for monolithic reactors due to the existing 3D hierarchical structures. Carbonized wood with superior electrical conductivity, chemical and mechanical stability, and tunable multifunctionality endows it as a monolithic reactor object to synthesize advanced materials for multiple purposes. This study constructed a carbonized monolithic microreactor for the continuous-flow catalytic synthesis of ethyl cinnamate, using the basswood column with a natural three-dimensional microchannel structure, which was carbonized in a nitrogen atmosphere at 700 ℃. The peristaltic pump tube is used to connect the metal coil and the carbonized monolithic microreactor in turn. The peristaltic pump sends the reaction liquid to the metal coil, and the oil bath pan heats the metal coil to preheat the reaction liquid. Subsequently, the reaction liquid enters the carbonized monolithic microreactor, and the oil bath circulation device heats the reactor to ensure the reaction temperature. The results indicate that the length and diameter of carbonized-wood columns were reduced from 200 and 40 mm to 165 and 29.6 mm, respectively, due to the pyrolysis of lignin, hemicellulose, and cellulose at elevated temperature. The resulting material not only preserves the well-aligned microchannel topology of the original wood, but also exhibits significantly enhanced properties, including high chemical stability, robust mechanical strength, and exceptional mass and heat transfer performance—laying a solid foundation for efficient continuous-flow catalytic processes. SEM characterization demonstrated the regular and hierarchical porous structures of carbonized column with abundant tubular channels (5-50 µm in diameter) in the wood growth direction and micro-sized pores (0.5-1 µm) inside tubular channels. The micro-sized pores on the tubular channels allowed the liquid substrates to enter the adjacent channels and generate fluid disturbance for improved mass transfer and enhanced catalytic capacity. Then, 96.5% of cinnamic acid conversion was reached with the molar ratio of cinnamic acid to ethanol at 1:20, the catalyst addition of concentrated H₂SO₄ (98 wt%) being 30 % of the mass of cinnamic acid, the reaction temperature of 100 ℃, substrate flow rate of 5 mL/min and the outlet pressure at 0.2 MPa. Under the continuous-flow reaction mode, a carbonized-wood monolithic microreactor induced a maximum TOF of 42.4 h⁻¹ for the catalyst of sulfuric acid, which was 11.7-22.3 times higher than that in batch-mode reaction. This carbonized monolithic microreactor exhibited excellent mechanical strength (4 538 N in load, 31.2 MPa in compressive strength, 3839 MPa in elastic modulus) and acid-base tolerance, which could maintain over 90% of cinnamic acid conversion after 10 consecutive runs. The microchannel reactor was subjected to immersion tests in both acidic and alkaline solutions of varying concentrations for 24 hours. After drying, its structural morphology remained fully intact, demonstrating exceptional resistance to corrosive chemical environments. These properties ensure long-term chemical stability under continuous operation, structural integrity against collapse or deformation caused by reactive fluid flow under process conditions. Besides, it can also be used for the efficient preparation of various flavor esters, such as ethyl acetate (93.5%), hexyl hexanoate (95.7%), iso-amyl p-methoxycinnamate (87.6%), ethyl hexanoate (78.9%), ethyl butyrate (92.0%), and cinnamic acid methylester (92.4%). Hence, the research developed a carbonized-wood monolithic microreactor with basswood as raw material, which was filled into a metal casing after elevated temperature carbonization. The reactor exhibited high mass and heat transfer efficiency, presented good mechanical properties, and acid and alkali resistance. The finding can provide a potential strategy for the efficient synthesis of flavor esters by combining continuous flow reaction and acid catalysis, in industrial applications in the field of food and cosmetics.