Evaluating the effect of aging process on the physicochemical characteristics of rice seeds by low field nuclear magnetic resonance and its imaging technique

Combined ultrasound and germination treatment on the fine structure of highland barley starch

Highland barley is a grain crop grown in Tibet, China. This study investigated the structure of highland barley starch using ultrasound (40 kHz, 40 min, 165.5 W) and germination treatments (30℃ with 80% relative humidity). The macroscopic morphology and the barley's fine and molecular structure were evaluated. After sequential ultrasound pretreatment and germination, a significant difference in moisture content and surface roughness was noted between highland barley and the other groups. All test groups showed an increased particle size distribution range with increasing germination time. FTIR results also indicated that after sequential ultrasound pretreatment and germination, the absorption intensity of the intramolecular hydroxyl (-OH) group of starch increased, and hydrogen bonding was stronger compared to the untreated germinated sample. In addition, XRD analysis revealed that starch crystallinity increased following sequential ultrasound treatment and germination, but a-type of crystallinity remained after sonication. Further, the Mw of sequential ultrasound pretreatment and germination at any time is higher than that of sequential germination and ultrasound. As a result of sequential ultrasound pretreatment and germination, changes in the content of chain length of barley starch were consistent with germination alone. At the same time, the average degree of polymerisation (DP) fluctuated slightly. Lastly, the starch was modified during the sonication process, either prior to or following sonication. Pretreatment with ultrasound illustrated a more profound effect on barley starch than sequential germination and ultrasound treatment. In conclusion, these results indicate that sequential ultrasound pretreatment and germination improve the fine structure of highland barley starch.

Early detection of cotton verticillium wilt based on root magnetic resonance images

Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.