Cropland water–land balance relationship can be regulated to maintain the cropland productivity, ecological stability, and sustainable land use. It is often required to optimize cropland spatial layout and crop planting structure in sustainable water and land resources, according to “Determining Land Use by Water Availability”. In this study, an integrated analytical framework was developed to explore the spatiotemporal characteristics of the balance between water and cultivated land resources. There was also a close relationship between crop water requirement, water deficit and surplus, drought events, and productivity loss. The research area was taken as the five major agricultural regions in northern China. Meteorological, crop planting structure, and gross primary productivity (GPP) were monthly collected from 2001 to 2022. The full-growing-season crop water requirements of winter wheat, spring maize, and summer maize were estimated using the FAO-56 crop coefficient. A decadal Crop Water Deficit and Surplus Index (CWDI) and its standardized form, the Standardized Crop Water Deficit and Surplus Index (SCWDI), were then constructed to integrate run theory. Empirical orthogonal function (EOF) analysis, correlation analysis, and a Copula–Bayesian conditional probability model were used to systematically identify the Spatiotemporal evolution of cropland water–land balance relationships, crop critical water-demand periods, and productivity loss risks under drought stress. The results showed that (1) multi-year mean full-growing-season water requirements of winter wheat and maize were 500 and 594 mm, respectively, particularly with 627 and 536 mm for spring and summer maize, respectively. Crop water requirements shared significant regional differences. In winter wheat, the water requirement followed the descending order of Huang–Huai–Hai Plain (521 mm) > Loess Plateau (514 mm) > Gansu–Xinjiang Region (467 mm), while spring maize shared the highest water requirement in the Gansu–Xinjiang Region (662 mm). (2) Average drought-event frequency ranged from 0.4 to 2.0 events per year. The Gansu–Xinjiang Region was characterized by a high proportion of extreme drought events, long duration, and high intensity, indicating a cumulative drought pattern, whereas the Huang–Huai–Hai Plain was dominated by high-frequency but short-duration drought events. (3) EOF decomposition showed that the first five modes cumulatively explained 73.7% of the total variance, with EOF1 and EOF2 accounting for 28.6% and 20.0%, respectively, indicating region-wide consistency and regional heterogeneity in interannual dry–wet variations of cropland. (4) Correlation analysis between SCWDI and standardized GPP (SGPP) showed that the key months were identified as July for spring and summer maize, while May for winter wheat. According to the cumulative effect, the water-demand stages of spring maize, summer maize, and winter wheat corresponded to May–June, June, and March–April, respectively. (5) SCWDI–SGPP dependence structures of spring maize and summer maize were best fitted by the Gaussian Copula, while the Frank Copula performed best for winter wheat. The average probabilities of productivity loss were 63%, 80%, and 44%, respectively, for spring maize, summer maize, and winter wheat under extreme drought conditions. The north-central Huang–Huai–Hai Plain and the Loess Plateau were identified as the drought stress conversion into productivity loss. The finding can provide a scientific basis for the optimal cropland layout and crop structure during drought risk prevention in northern China.

Immunization injection is of ten require d to prevent and control the diseases in pig farming. However, current needle injection still relies on manual labor in pig farms, leading to serious challenges, such as high risk of needle breakage, severe cross-infection, low efficiency, time-consuming, and labor-intensive operation, as well as missed or incorrect injections. In this study, a control system was proposed for vision-based positioning and motion in a needle-free injection robot under confined stall breeding scenarios. According to the workflow of swine immunization, the key components were selected to integrate the navigation system of the robot. Its parameters were then determined to improve the needle-free injection module. The motion range of the robotic arm was simulated using MATLAB. The rotation angles of its six axes were constrained to prevent collision between the robotic arm and confined stall. A precise localization was proposed for the needle-free injection site on pigs. The YOLOv8n framework was improved to solve the missed and false detections caused by blurred features of the tail root and susceptibility to dirt interference. The improved network was used to extract the tail root region from pig images. A point set of the hip muscle injection area was constructed for vertical injection. The least squares method (LSM) was applied to fit the surface point set, enabling accurate calculation of the injection point and posture. A control system was developed to accurately track the injection point in the needle-free injection robot, due to the random movement of pigs during operation. According to feeding system, the injection was divided into two phases: a pre-injection and an injection phase. In the pre-injection phase, the tail root position was continuously detected to perform visual servoing control of robotic arm, thereby tracking the injection point in real time. In the injection phase, the robot moved into the injection point, and then performed the injection, according to the last detected pose. The critical distance of phase transition was determined to be 15 cm using depth camera and triangulation. The robot operating system (ROS) was used to integrate navigation, visual detection, and robotic arm control algorithms, enabling the robot to follow a predetermined trajectory and then perform needle-free injection on pigs. Experiments were conducted at a pig farm in Qinhuangdao City, Hebei Province, China. The experimental results showed that the tail root detection algorithm achieved an accuracy of 95.8%, a recall of 93.5%, and a mean average precision (mAP) of 97.1%. The overall injection success rate of the robot was 93.3%, of which 95.2% were vertical injections. The maximum injection deviations in the X, Y, and Z axes were 2.9, 3.7, and 1.9 cm, respectively, corresponding to average deviations of 1.30, 1.91, and 0.59 cm. The injection accuracy was fully met the requirements of swine immunization. A closed-loop control system was realized from chassis navigation, visual recognition, dynamic tracking to precise triggering for the needle-free injection in real scenarios. The findings can also offer the technical and engineering reference for the large-scale intelligent farming.

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.

Recognition accuracy of small targets is often required for dense fruit distribution in natural orchard environments. Therefore, a flat peach detection model based on an improved YOLOv8 architecture, termed CCGs-YOLO, was proposed in this study. The proposed model integrates the MetaFormer framework with a convolutional gated linear unit module. A hybrid module combining convolution and attention mechanisms was introduced to enhance spatial feature extraction and improve feature representation under complex background conditions. Meanwhile, a channel-aware module was incorporated to simulate inter-channel dependencies, thereby improving the discrimination capability between fruit targets and cluttered backgrounds. Specifically, the C2f_ConvFormer module was employed to simultaneously capture local and global contextual information, while the C2f_CaFormer module was introduced to enhance channel interaction and feature aggregation. In addition, a convolutional gated linear unit mechanism was embedded into the network to improve feature selection capability and robustness against background noise. To address the small object detection problem in densely distributed fruit scenarios, localization accuracy was further improved. An optimized regression loss function based on an inner-overlap constraint, named Inner-CIoU, was adopted to achieve more accurate bounding box regression and reduce localization errors caused by overlapping targets. Experimental results demonstrated that the ConvFormer module improved the F1-score to 90.44% and the mAP50 to 96.40%, indicating enhanced feature extraction capability. The CaFormer module increased the F1-score from 89.96% to 90.63%, while the mAP50 further improved to 96.12%, demonstrating effective channel modeling capability under relatively high inference efficiency. When only the convolutional gated linear unit mechanism was applied, the F1-score reached 90.20% and the mAP50 achieved 96.12%, verifying its effectiveness in enhancing feature representation. Furthermore, the combination of CaFormer and convolutional gated linear unit improved the Precision to 91.47%, the F1-score to 90.77%, and the mAP50 to 96.24%, demonstrating the complementary advantages of channel modeling and gated feature selection. In terms of localization performance, Inner-CIoU improved both mAP and model convergence stability compared with the conventional CIoU loss function. After integrating all improved components, the model achieved relatively better overall performance. Precision reached 93.07%, representing an increase of 3.31 percentage points compared with the baseline model. The F1-score reached 90.87%, while the mAP50 achieved 96.24%. Meanwhile, the model size was reduced from 5.97 MB to 4.88 MB, and the number of parameters decreased from 3.01 million to 2.42 million, indicating that the proposed model possesses favorable lightweight characteristics while maintaining relatively high inference speed of 362.07 FPS. In addition, comparative experiments were conducted with several mainstream models, including different versions of the YOLO series. Under challenging scenarios such as occlusion, small targets, dense distribution, and edge targets, the proposed model achieved relatively superior performance in terms of Precision, F1-score, and mAP, demonstrating improved detection stability and feature perception capability. Visualization analysis further indicated that the improved model could focus more accurately on fruit regions and suppress background interference to a certain extent. Furthermore, deployment experiments on edge computing devices demonstrated that the proposed model could still maintain relatively high detection accuracy and stable performance under practical application conditions, with the mAP50-95 reaching 88.82%, indicating potential for real-world applications. Overall, the proposed model effectively balanced detection accuracy, model lightweight characteristics, and computational efficiency, demonstrating good robustness and adaptability in complex orchard environments. The proposed approach can provide a feasible technical solution for rapid and accurate fruit recognition in flat peach harvesting.

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.

Asparagus harvesting can be confined to the efficacy of robotic vision in recent years. Asparagus spears are characterized by a slender morphology in their natural growth state. These tender stems are highly prone to mutual occlusion and overlapping when growing densely in field conditions. Furthermore, the stout mother stems can simultaneously present as the complex background interference. Collectively, it is often required for the high accuracy of the multi-target segmentation and recognition using machine vision. In this study, the lightweight instance segmentation model (YOLO11n-seg) was adopted as a baseline, in order to improve the precise positioning and harvesting performance of the robotic end-effector. Consequently, an optimized model named YOLO11n-SAL was also proposed to specifically tailor the slender, occluded targets with high fidelity. Two modules were introduced to enhance the feature extraction and attention mechanisms in the architectural framework. Firstly, the multi-scale edge enhancement Module (MEEM) was conceptually designed and integrated in order to mitigate the challenge wherein the edge features of the slender asparagus targets were inherently weak and easily lost during convolutional operations. Multi-scale decomposition was performed on the convolutional feature maps. The MEEM effectively extracted and intensified the edge and contour information before feature fusion. The sensitivity to the target boundaries was significantly elevated for the high segmentation precision, thereby enhancing the perceptual capability of the targets with the slender morphological structures. Secondly, the separated and enhancement attention module (SEAM) was introduced to rectify the feature confusion and data incompleteness caused by inter-target occlusion. Attention separation over both channel and spatial dimensions was also utilized to adaptively perceive the local and global features of the occluded asparagus at the varying scales. These features were selectively enhanced and effectively fused to better position the visible subjects of the partially masked targets, while suppressing the background noise and distractor information. The robust performance of the detection and recognition was maintained even within the complex and cluttered environments. A series of experiments was conducted to verify the effectiveness of the improved model. Quantitative evaluation results indicate that the improved YOLO11n-SAL model achieved significant gains over all key performance indicators, compared with the baseline model. In the detection task of the target bounding box, the superior performance was achieved with a detection precision of 94.2%, a recall rate of 83.1%, a mean average precision at IoU threshold 0.5 (mAP_0.5) of 91.2%, and a mean average precision at IoU threshold 0.5-0.95(mAP_0.5-0.95) of 76.2%. In the more granular instance mask segmentation, the model also performed impressively. The segmentation precision, recall, mAP_0.5 and mAP_0.5-0.95 reached 93.4%, 77.9%, 90.7%, and 62.7%, respectively. Furthermore, the heatmap analysis demonstrated that the YOLO11n-SAL model was markedly improved to perceive the asparagus edge features over different scenarios, with the superior multi-target segmentation and recognition under occluded conditions. The high accuracy of the segmentation and recognition was achieved to reduce the interference in the complex multi-scenario environments, compared with the baseline. Finally, a series of asparagus recognition, positioning, harvesting, and grasping trials were carried out using depth cameras and mechanical arms, in order to validate the cognition and position performance in the actual deployment scenarios. The empirical results showed that a positioning success rate of not less than 90% was accompanied by effective harvesting and grasping performance. These findings can provide reliable technical support for the advancement of robotic harvesting in precision agriculture.

The meat duck industry in China has contributed over 82% of the total slaughter volume worldwide. Therefore, an optimal air temperature is often required for the comfort and survival of meat ducks during breeding. However, the air temperature is susceptible to multiple factors, such as relative humidity and illumination intensity. It is a high demand to timely and accurately predict air temperature for the high-density healthy breeding. However, conventional temperature prediction has been limited to low accuracy, robustness, and generalization. In this study, a hybrid 1DCNN-BiLSTM-DQN model was proposed to integrate with a one-dimensional convolutional neural network (1DCNN), a bidirectional long short-term memory network (BiLSTM), and a deep Q-network (DQN). Duck-house temperature was accurately predicted after model construction. The temperature time-series signal was also decomposed into high- and low-frequency components via the discrete Fourier transform (DFT). Given that the high-frequency component represented short-term fluctuations, the 1DCNN was used to extract local features from the high-frequency component; whereas the low-frequency component represented long-term fluctuations, the BiLSTM was used to extract long-sequence dependency features from the low-frequency component. Subsequently, the two sets of features were fused using a concatenation model. And finally, the temperature prediction value was obtained after the mapping of a fully connected layer. Furthermore, the DQN algorithm was introduced to construct an agent for iterative optimization of the hyperparameters. An adaptive mechanism was optimized with an 8-dimensional state space and a 12-dimensional action space, enabling dynamic optimization of key hyperparameters, such as the learning rate, number of hidden layers, dropout rate, and network architecture. Thereby, the prediction robustness of the model was improved under scenarios of seasonal transitions and extreme weather. The indoor and outdoor temperature data of net-raised duck houses were collected in Gaoyou City, Yangzhou City, Jiangsu Province, from March 22, 2025, to March 22, 2026. The results demonstrated that the 1DCNN-BiLSTM-DQN model achieved a coefficient of determination (R²) of 0.993 with an optimal input step size of 48. The MAE and RMSE were superior to the conventional models, such as the Temporal Convolutional Network and Transformer. Specifically, the 1DCNN-BiLSTM-DQN model exhibited the following improvements under different weather conditions: On cloudy days, the MAE and RMSE decreased from 0.29 °C to 0.18 °C, and from 0.36 °C to 0.23 °C, whereas the R² increased from 0.77 to 0.91; On sunny days, the MAE and RMSE decreased from 0.48 °C to 0.23 °C, and from 0.59 °C to 0.31 °C, whereas the R² increased from 0.79 to 0.94; On rainy days, the MAE and RMSE decreased from 0.54 °C to 0.33 °C, and from 0.70 °C to 0.46 °C, whereas the R² increased from 0.72 to 0.89. The combined 1DCNN-BiLSTM prediction model achieved better prediction performance compared with the conventional models, such as BiLSTM, 1DCNN, TCN, and Transformer. In summary, the 1DCNN-BiLSTM-DQN model can be expected to predict the temperature in duck houses. The findings can also provide data support for early environmental regulation, thereby reducing the risk of environmental stress in meat ducks.

Ecological ditch–pond systems are important measures for controlling agricultural non-point source pollution, yet their practical application is constrained by unstable purification performance and large land occupancy. The effective application of such systems in irrigation districts depends not only on the design and operation management of individual units, but also significantly on their spatial layout (including system area and unit connection pattern). Existing studies often fail to adequately capture the multi-level dynamic responses of water volume and water quality in such systems, which hinders their support for spatial layout optimization. Against this backdrop, this study proposes a system dynamics-based simulation method for optimizing the spatial layout of ditch-pond systems in irrigation districts. A field-ditch-pond system model was developed using the system dynamics simulation tool Vensim, integrating water balance, pollutant removal processes, and hydraulic connections among ditches and ponds. The water depth in the paddy model was determined by inflows, outflows, and water consumption during each time step, while the total nitrogen and total phosphorus concentrations were simulated by considering fertilization, first-order pollutant decay, and inputs from rainfall and irrigation. For the ditch–pond unit model, water volume changes were governed by rainfall, evapotranspiration, seepage, upstream inflow, and drainage discharge. Pollutant concentrations in the ditch–pond unit model were calculated using two modes: static storage-based reduction and dynamic drainage-based reduction. The paddy and ditch–pond unit models were linked through system dynamics into an integrated field-ditch-pond system model, which was calibrated and validated using field monitoring data. A case study was conducted in a typical double-cropping paddy high-standard farmland demonstration area in southern China. The model verification results show that the developed model can effectively simulate the dynamic variations of water volume and pollutant concentrations in the system. The case analysis results show that: 1) With increasing ditch-pond to paddy area ratio, nitrogen and phosphorus removal rates rise, but the rate of increase gradually slows down, suggesting an optimal range of 5%-9%; 2) During the late rice season or under larger area ratios, concentrating wetlands in a single drainage path results in a significantly lower removal rate compared to other layouts; 3) For practical implementation, it is recommended to first determine an appropriate area ratio based on target pollutant reduction goals. Subsequently, wetlands may be placed either at the main drainage outlet or distributed in parallel across different drainage pathways, depending on site-specific conditions. The findings provide a methodological reference for modeling multi-level wetland systems and offer a scientific basis for ecological control of agricultural non-point source pollution. Future work may further refine the simulation of water cycling and pollutant transformation processes within field-ditch-pond systems to enhance model accuracy and applicability.

Wheel-type rice transplanters have been widely used in mechanized paddy-field transplanting because of their flexible operation and convenient field transfer. However, during paddy-field operation, the unstable adhesion state between the driving wheels and saturated paddy soil may induce lateral deviation, longitudinal slip, and row-spacing errors. To clarify the slip behavior of a wheel-type rice transplanter under different soil moisture contents, traveling speeds, and whole-machine masses, this study analyzed the variation characteristics of the slip ratios of a wheel-type rice transplanter. A Xinyang 2ZG-6D1(G4) wheel-type rice transplanter was selected as the research object. A whole-machine multi-body dynamics model was established in RecurDyn, and a layered paddy-soil particle bed was constructed in EDEM. The Hertz-Mindlin with JKR contact model was adopted as the soil contact model to describe the adhesive contact behavior among paddy-soil particles and between soil particles and soil-engaging components. Compression tests and direct shear tests were conducted to calibrate the discrete element parameters of paddy soil, thereby improving the reliability of the soil model. The bidirectional coupling between the transplanter model and the soil model was realized through the EDEM-RecurDyn coupling interface. Soil moisture content, traveling speed, and whole-machine mass were selected as experimental factors, while lateral slip ratio and longitudinal slip ratio were used as evaluation indexes. Based on the calibrated discrete element method and multi-body dynamics (DEM-MBD) coupling model, a Box-Behnken response surface experiment was conducted to investigate the effects and interaction mechanisms of the three factors on the two slip indexes. Regression models of the lateral and longitudinal slip ratios were established, and multi-objective optimization was carried out to obtain a suitable parameter combination. Field tests were finally performed to verify the prediction accuracy of the coupled simulation model. The results showed that soil moisture content had the most significant effect on both lateral and longitudinal slip ratios, with contribution rates of 20.54% and 35.74%, respectively. The interaction between soil moisture content and whole-machine mass also had an obvious influence on the two slip ratios, indicating that the effect of machine load on wheel-soil interaction depended strongly on the moisture state of paddy soil. With increasing soil moisture content, the bearing, shear, and adhesion characteristics of the soil changed, further affecting wheel sinkage, soil adhesion, and driving stability. Within the experimental range, when the soil moisture content was 32%, the traveling speed was 0.54 m/s, and the whole-machine mass was 854 kg, the lateral and longitudinal slip ratios showed relatively good comprehensive performance; that is, both indexes remained at relatively low levels under the multi-objective optimization constraints. The field validation results showed that the measured lateral slip ratio was 1.55%, while the simulated value was 1.36%, with a relative error of 12.25%. The measured longitudinal slip ratio was 11.48%, while the simulated value was 10.15%, with a relative error of 11.59%. Both errors were within 15%, indicating that the established DEM-MBD coupling model can reasonably predict the slip ratios of a wheel-type rice transplanter under paddy-field conditions. This study provides a feasible simulation method for analyzing wheel-soil interaction in paddy fields and offers a reference for the design of walking systems and the optimization of operating parameters for paddy-field machinery.

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.