The maturity of tomato fruit is normally characterized by external color, and it is often difficult to know when fruit have achieved commercial maturity or become over-mature. The internal structure of tomato fruit changes during development and this study investigates the utility of nondestructive measurement of tomato fruit structure as a function of maturity using magnetic resonance imaging (MRI). The objective of this work is to use analysis of internal tomato fruit structural measurements to characterize maturity. Intact cherry tomato fruit were harvested at six different maturity stages. At each stage of maturity, the internal structure of the fruit was measured using a series of two-dimensional (2D) magnetic resonance (MR) images. Qualitative and quantitative image analyses were performed to correlate internal fruit structure with maturity. Internal structural changes observed in the pericarp region of the tomato fruit are highly correlated with fruit maturity. MR image information combined with classical analysis techniques provides a more complete understanding of structure and physicochemical changes in tomato fruit during maturation. This study demonstrates that MRI is a useful analytical tool to characterize internal changes in agricultural produce as the produce matures. This technique can be applied to almost any agricultural produce to monitor internal physical changes due to external impact, maturity stage, variation in climate, storage time, and condition, or other factors impacting quality.

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.

This study investigated the application of robotic automation in food manufacturing, focusing on enhancing tray transporting operations through a simulation-based approach. The findings primarily focused on bakery production but also demonstrate broader applicability to other sectors that involve repetitive and labor-intensive tasks. The researchers analyzed worker fatigue and limited productivity associated with manual tray handling. To evaluate these issues, simulations were conducted for two scenarios (Case A and Case B), applying robotic automation systems at different stages of production. Key performance indicators (throughput and utilization rates) were analyzed to assess improvements in process efficiency and reductions in worker strain. The results showed that robotic automation significantly increased throughput by 83.7% in simpler processes and by 27.1% in more complex ones, highlighting the impact of task complexity on automation effectiveness. Workforce demands decreased and demonstrated the potential of automation to alleviate physical strain in repetitive tasks. Simulations provided insights into workflow optimization, confirming their value as reliable tools for planning and refining automation strategies. The proposed framework offers a flexible and scalable solution for enhancing efficiency and consistency in manufacturing. Future research should apply similar approaches to other industries and explore the integration of human and robotic labor to further optimize safety, productivity, and cost effectiveness.

This study intended to analyze growth changes and antioxidant activities of Romaine lettuce by different treatments of microbubble plasma discharge water and bioblock (far-infrared radiation). Hydroponics were performed in smart farm environments, and the used Romaine lettuce was the Caesar's Green variety. The treatment conditions of Romaine lettuce (TRL) are as follows: TRL-1 was treated with plasma discharge water and 30 bioblocks, and TRL-2 was treated with plasma discharge water and 40 bioblocks. On the other hand, TRL-3 was not treated. Romaine lettuce was grown for a total of 49 days and the growth changes were measured at every seven days. In growth changes, TRL-2 was significantly higher (p<0.05) than other treatments in germination rate and weight, leaf length, and leaf count. The total polyphenol and flavonoid contents were analyzed as similar levels between the samples, but TRL-2 showed higher antioxidant activities in ABTS radical scavenging (%) compared to the other samples. As a result of this study, TRL-2 conditions are evaluated to affect the growth and antioxidant functions of Romaine lettuce.