Aquatic animal products are vital to global food security and nutrition, necessitating accurate, scalable, and non-destructive methods for quality assessment in aquaculture. Conventional techniques such as dissection and biochemical analysis are invasive, labor-intensive, and unsuitable for real-time or high-throughput decision-making. This review synthesizes six major categories of non-destructive technologies—electrical, spectroscopic, natural sensory, acoustic, radiographic, and infrared and microwave—classified by their underlying sensing mechanisms and therefore differing measurement capabilities and deployment feasibilities. To support objective technology selection, an Analytic Hierarchy Process (AHP) framework was developed using general performance criteria (cost, accuracy, speed, usability) and one decision-critical application-specific criterion (non-invasiveness), and was demonstrated for ovarian maturation staging in mud crabs by ranking 19 candidate techniques. Accuracy had the highest weight (0.416), but non-invasiveness (0.224) and usability (0.197) substantially influenced the final ranking, illustrating how operational and welfare constraints could shift preferred solutions despite differences in analytical accuracy. Based on the global priority weights (GA), computer vision (CV) was identified as the most suitable option (GA = 0.076), balancing affordability, throughput, ease of deployment, and animal welfare compatibility, whereas high-end modalities such as nuclear magnetic resonance (NMR; GA = 0.073) and computed tomography (CT; GA = 0.070) were constrained by cost and operational complexity. Overall, this review–AHP–case study pipeline provides a transparent and reproducible decision-support basis for selecting non-destructive technologies across aquaculture species and quality targets.

This research presents a telescopic soft gripper designed to assist farmers in the harvesting of mangosteens. Subsequent to the outbreak of a pandemic, like the novel coronavirus (SARS-CoV-2), producers are encountering a workforce shortage during the harvesting of mangosteen. The labor crisis is currently becoming more acute, but the competition for mangosteen exports is increasing, requiring the expansion of mangosteen agricultural areas, which will ultimately give rise to a labor shortage. The gripper is fabricated from hyperelastic material, a material that offers flexibility and softness, making it ideal for delicate objects. Conventional soft grippers lack the telescopic design, which enables extension and retraction, allowing for the handling of various object sizes. The gripper's design optimization involved the evaluation of nine models with different internal inclinations and material thicknesses. Finite Element Analysis (FEA) was employed to simulate the deformation and stress responses. The optimized model, in comparison to nine other models, possesses a thickness of 2.25 mm and an internal inclination of 10 degrees, facilitating high deformation with acceptable stress. It has been demonstrated that the gripper can deform up to 132 mm, a finding that has been validated through experimentation. The experimental validation was conducted to corroborate these findings, demonstrating the gripper's capacity to securely grasp objects with diameters ranging from 50 mm to 70 mm and weights up to 22 N. Furthermore, the gripper's efficacy was assessed in a mangosteen harvesting scenario, where it demonstrated a capability to successfully harvest the fruit within a span of two seconds. The gripper's design is characterized by its compactness, low production cost, and ease of use, rendering it highly practical for agricultural applications in confined spaces. The telescopic soft gripper under consideration offers a versatile and scalable solution for harvesting a wide range of crops of varying sizes, positioning it as a valuable tool for future agricultural automation.

Wood-plastic composites (WPCs) possess a diverse arrange of advantageous characteristics resulting from the combination of wood and plastic. These include a prolonged lifespan, cost-effectiveness, and environmental friendliness. However, the net shape can only be roughly achieved by the extrusion method used to make WPCs. WPCs require additional machining to comply with the dimensional accuracy and assembly conditions. As a result, more research into the cutting process of this composite is needed. In this work, abrasive water jet cutting was used to cut WPCs manufactured from rubber wood flour and polypropylene (PP). The emphasis was on two distinctive architectures of 1-layered and 3-layered WPCs with a thickness of 10 mm. The effects of cutting factors including water pressure, traverse speed, and abrasive mass flow rate were taken into account. The study employed a full factorial analysis to investigate the correlation between cutting parameters and cutting performance, including kerf characteristics and machined surface roughness. The abrasive and water pressure in the experiment had a significant effect in cutting both materials. In some cutting conditions, without an abrasive, non-through cutting happens, particular for the 3-layered WPC structures, that are particularly strong. An increase in water pressure decreased machining surface roughness and kerf width. The cutting parameters of 350 MPa water pressure, 30 mm/s traverse speed, and 6.67 g/s abrasive flow rate were suggested for both the 1-layered and 3-layered WPCs structures. This procedure was considered suitable for post-cutting without requiring for surface finishing.

The purpose of the research is to evaluate image processing and machine learning techniques for classifying fish size. In this study, the sizes of mackerel fish were classified into three categories based on their weight range: small, medium, and large. Initially, the fish were captured on top and side views using a light control box to regulate image quality. The fish images were then analysed by image processing and machine learning techniques using the MATLAB software to acquire fish characteristics. Five fish qualities were observed including fish area, fish perimeter, fish length, fish width, and the length-to-width ratio in top and side views. For the purpose of categorizing fish into separate groups, statistical analysis was used to determine the relationship between fish characteristics and size. The correlation analysis between fish attributes and size revealed that the fish area in top view exhibited the highest correlation value, following by the fish width in top view. Combining image processing and regression analysis can be used to classify fish by size. On the other hand, the decision tree algorithm was implemented to allocate fish sizes using machine learning. It was discovered that both image processing and machine learning can be used to classify fish by size. The classification accuracy of both techniques exceeded 90%, which was satisfactory. This can be further applied in automated fish-processing lines to supplant manual labours.

Ovarian maturity in live female mud crabs (Scylla paramamosain) strongly affects harvest decisions and market value. Current ovarian maturity assessment relies mainly on expert-dependent methods that are subjective and destructive. Therefore, this study aimed to develop an interpretable, non-destructive image-based framework to classify crab ovarian maturity as immature or mature. A total of 240 crab image sets acquired using ventral external-view, dorsal external-view, and dorsal transillumination imaging were retained for analysis. Six primary morphometric features were semi-manually extracted from these views. External-view images quantified carapace width (CW), abdomen width (AW), abdomen area (AA), and sternum area (SA). Dorsal transillumination images yielded carapace area (CA) and ovary area (OA), an internal cue visualized through the intact carapace. To mitigate body-size variation, three ratio-based features—abdomen–carapace width ratio (ACWR), abdomen–sternum area ratio (ASAR), and ovary–carapace area ratio (OCAR)—were calculated. Between-class comparisons and correlation analyses were performed to guide candidate feature-set construction. Because OA and OCAR were strongly correlated, two reduced feature sets (Reduced 1 and Reduced 2) were designed to compare absolute ovary area with normalized ovary occupancy. Five feature sets—Raw, Ratio, Combined, Reduced 1, and Reduced 2—were evaluated using logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers. The Combined feature set, integrating all primary and ratio-based features, achieved the strongest mean cross-validated performance when paired with LR. On the held-out test set (n = 40), the final Combined-LR model achieved 0.950 accuracy and 0.997 ROC–AUC. On an independent practical implementation set (n = 40), the model correctly classified 39 specimens, achieving 0.975 accuracy. These findings may support non-destructive ovarian maturity screening and commercial grading in mud crab aquaculture.