Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly.

No abstract

Alcohol and drug overdoses cause liver diseases such as cirrhosis, hepatitis, and liver cancer globally. In particular, an overdose of acetaminophen (APAP), which is generally used as an analgesic and antipyretic agent, is a major cause of acute hepatitis, and cases of APAP-induced liver damage are steadily increasing. Potential antioxidants may inhibit the generation of free radicals and prevent drug-induced liver damage. Among plant-derived natural materials, radishes (RJ) and turnips (RG) have anti-inflammatory, anticancer, and antioxidant properties due to the presence of functional ingredients, such as glucosinolate and isothiocyanate. Although various functions have been reported, in vivo studies on the antioxidant activity of radishes are insufficient. Therefore, we aim to evaluate the hepatoprotective effects of RG and RJ in APAP-induced liver-damaged mice. RG and RJ extracts markedly improved the histological status, such as inflammation and infiltration, of mice liver tissue, significantly decreased the levels of alanine transaminase, aspartate aminotransferase, and malondialdehyde, and significantly increased the levels of glutathione, superoxide dismutase and catalase in the APAP-induced liver-damaged mice. In addition, RG and RJ extracts significantly increased the expression of Nrf-2 and HO-1, which are antioxidative-related factors, and regulated the BAX and BCL-2, thereby showing anti-apoptosis activity. These results indicated that RG and RJ extracts protected mice against acute liver injury, attributed to a reduction in both oxidative stress and apoptosis. These findings have clinical implications for the use of RG and RJ extracts as potential natural candidates for developing hepatoprotective agents.

No abstract

No abstract