Three-dimensional (3D) food printing (3DFP) is an emerging technology that enables the creation of personalized and functional foods by precisely controlling nutritional content and shape. This study investigated the 3D printability and rheological behavior of cereal–legume starch-based gels formulated with germinated brown rice (GBR) and red adzuki bean (RAB) flours, supplemented with xanthan and guar gums as functional additives. The physicochemical and structural properties of the gels were characterized through FT-IR, rheology, texture analysis, SEM, and sensory evaluation. In addition, the 3D printing fidelity, rheological behavior, color attributes, textural properties, microstructure, and sensory scoring of the printed products were evaluated. The results indicated that the gels exhibited pseudoplastic behavior, with the RABF/GBRF ratio of 1:2 (RG1:2) formulation showing optimal color properties (ΔE* = 0.60 ± 0.86) and the RABF/GBRF ratio of 2:1 (RG2:1) formulation demonstrating superior printing fidelity and structural stability (printing accuracy = 99.37 ± 0.39%). The gels’ mechanical properties, such as hardness and chewiness, were significantly influenced by the RABF and GBRF ratios, with RG2:1 exhibiting the highest hardness (1066.74 ± 102.09) and RG1:2 showing the best springiness (0.64 ± 0.10). The sensory evaluation results indicated that the RABF/GBRF ratios of 1:1 (RG1:1) and RG1:2 had relatively high overall acceptance scores. These findings indicate that specific ratios of RABF and GBRF improve the 3D printability and textural properties of cereal–legume starch-based gels, enhancing their suitability for 3D food printing applications. This study provides valuable insights into the development of personalized and functional cereal–legume starch-based foods using 3DFP technology.

Germinated brown rice (GBR), rich in high starch content and bioactive compounds, has excellent gel-forming properties, rendering it highly promising for applications in food 3D printing, a cutting-edge personalized manufacturing technology. This study systematically investigates the effects of different concentrations of konjac glucomannan (KGM) and curdlan (CD) blends on the 3D printing performance and physicochemical properties of GBR gel. The results indicated that the appropriate addition of KGM/CD blends significantly enhances the printing accuracy and shape retention of GBR gel. Specifically, under the KGM to CD ratio of 3:1 (KC3) formulation obtained by combining 2.25% KGM and 0.75% CD, the printing accuracy was highest with a minimized error of 4.97 ± 0.45%, and optimal structural stability was maintained within 5 h post-printing. Rheological measurements revealed that the flow behavior index (n) of the KC3 system was 0.049 ± 0.014, indicating superior flowability and significantly improved overall rheological stability. Additionally, the blend system not only increased the hardness and gel elasticity of the GBR gel but also significantly enhanced its cohesiveness and adhesiveness, reaching the highest values of 0.323 ± 0.02 and −217.488 ± 22.499, respectively, in the KC3 formulation. Further thermal analysis, low-field nuclear magnetic resonance analysis, along with Fourier-transform infrared spectroscopy and scanning electron microscopy observations, collectively demonstrated that the KGM/CD blend effectively reinforced the stability of the GBR gel network structure. These findings provide theoretical support for optimizing GBR applications in food 3D printing.

Aiming at the issues of complex manufacturing processes and unstable bonding after individual manufacturing in current soft manipulator forming methods, this study investigated the mechanical characteristics of a pneumatically driven soft-picking manipulator formed by the stereolithography (SLA) process and evaluated its application in grasping fruits and vegetables. The soft-picking manipulator mainly consists of three soft actuators designed in an integrated folded structure to simplify the manufacturing process compared to a conventional one. A finite element model (FEM) of the actuator was created to analyze the bending deformation capability under different pressures, and the simulated results match well with the experimental ones. Under the 60 kPa pressure, the maximum grasping force for fingertip- and envelope-grasping is 3.94 N and 8.87 N, respectively. The grasping tests of several fruits and vegetables of different weights and sizes by the soft manipulator were examined, and the results showed that the manipulator has strong adaptability. For spherical and elongated fruits and vegetables, the completion time for grasping is approximately 8.59 ± 1.26 s and 10.99 ± 1.79 s, respectively, and for irregularly shaped ones, the pressure is increased accordingly to the increased grasping stability. This study may provide a basis for the development of a soft manipulator for sorting and picking fruits and vegetables.

Appropriate calcium treatments help maintain the appearance, nutritional quality, and postharvest quality of apples, reducing losses during storage. This study investigated the effects of different calcium preparations on the fresh-cut quality and ultrastructure of ‘Starkrimson’ apples. The treatments included control (CK), calcium chloride (T1), sorbitol-chelated calcium (T2), and calcium nitrate (T3). The results demonstrated that sorbitol-chelated calcium significantly inhibited the decline in fresh-cut firmness and pectin content while reducing the increase in cellulose content and minimizing ultrastructural damage. Apples treated with sorbitol-chelated calcium maintained the best fresh-cut hardness and soluble pectin contents, which were 35.71% and 15.42% higher than that of CK on the 12th day, and the cellulose was 27.08% lower than that of CK. Under transmission electron microscopy, the pulp cell surface in the T2 group remained intact, with no bending or deformation, and the middle lamella was well preserved. Additionally, T2 treatment promoted the expression of aroma-related genes during fruit storage. Sorbitol-chelated calcium effectively preserved color and significantly reduced the browning and microbial spoilage of fresh-cut apples, particularly postharvest pathogen growth. The study demonstrates that sorbitol-chelated calcium preserves fresh-cut apple quality by reinforcing cell wall integrity through calcium-mediated crosslinking, suppressing pectin degradation and cellulose accumulation, and activating aroma-related genes (AAT1, AAT2, LOX) to enhance volatile synthesis, thereby reducing microbial spoilage and enzymatic browning during storage.

In the intelligent tomato-picking scenario, challenges such as insufficient accuracy in recognizing the growth pose of target tomatoes and inaccurate positioning of picking and grasping points have led to low efficiency in automated picking. To address these issues, this paper introduces an object detection optimization model based on Yolov8s, termed YOLOv8S-ECC. The model focuses on “Judging tomato pose by the spatial vector of the relative position between the calyx and the center point of the fruit,” aiming to enhance high-precision positioning of both the tomato calyx and fruit, thereby laying the groundwork for subsequent pose judgment and picking point positioning. We have integrated the ECA (Efficient Channel Attention) and Coordinate attention mechanisms into the Backbone network and introduced the CBAM (Convolutional Block Attention Module) attention mechanism into the Neck network. The combined effect of these attention mechanisms effectively overcomes the recognition challenges posed by the calyx’s color texture, which closely resembles the environment. This integration has also enhanced the model’s robustness in complex field environments. Test results indicate significant improvements: the accuracy rate, recall rate, and mAP@50 for detecting tomato fruits and calyces are 81.7% and 87.5%, 92.7% and 85.9%, and 89.7% and 91.3%, respectively, compared to the original model. By encapsulating the algorithm and integrating it with the picking robot, tests in a simulated environment (different lighting conditions and foliage occlusion situations) show picking success rates of 93.02%, with an average picking operation time of 14.2 ± 0.855 s, including an image recognition and processing time of 0.035 s. This research offers an effective technical solution for high-precision visual perception and pose judgment in fruit and vegetable picking robots, contributing to improved quality in tomato industry picking operations.

During cherry-tomato-bunch harvesting, clustered fruits, thin stems, and ambiguous fruit–stem junctions cause missed detections and errors in picking-point localization. To address these challenges, we propose TSP-Net, a structure-aware and geometry-constrained detection network built on YOLOv11n, together with an ROI heat-map regression module for picking-point localization. TSP-Net integrates the Truss-aware Multi-scale Ghost Cross Stage Partial (TMG-CSP) module into the backbone to strengthen structural features of fruit edges, small stems, and clusters. It introduces the Truss-oriented Axial-Local Attention (TALA) module during feature fusion to capture fruit-arrangement direction and local occlusion boundaries. We also design Physics-informed Truss Consistency (PTC) Loss to regularize detections by enforcing consistency with fruit-cluster morphology through constraints on center distribution, inter-fruit spacing, and scale continuity. For picking-point localization, local ROIs are extracted from detection outputs, Gaussian heat maps are predicted, and continuous coordinates are decoded using soft-argmax. In the controlled evaluation, TSP-Net attains precision 81.81%, recall 73.21%, mAP@50 79.53%, and mAP@50:95 64.83%, which exceed the corresponding YOLOv11n results by 0.83, 0.32, 1.29, and 1.03 percentage points, respectively. The model size is reduced from 5.23 MB to 5.08 MB, the parameter count falls from 2.59 M to 2.49 M, and computational cost declines from 6.4 to 6.0 GFLOPs. On the common valid-ROI test set, the proposed heat-map localization yields a mean localization error of 47.2 px, a median error of 24.2 px, and PCK@20, PCK@50, and PCK@100 of 45.1%, 68.4%, and 85.5%, respectively. Under identical ROI conditions, this heat-map method reduces the mean localization error by 11.3 px and raises PCK@20 by 14.9 percentage points compared with direct coordinate regression. In summary, the proposed approach enhances cherry-tomato-bunch detection and 2D picking-point candidate localization while meeting lightweight constraints, offering a structure-aware visual perception solution for greenhouse cherry-tomato harvesting.