The content of flavonoids in mesophyll cells of wheat was studied under the condition of enhanced UV-B radiation intensity. In this experiment, four groups of six days of control were treated with He-Ne laser group (L), enhanced UV-B radiation group (B), He-Ne laser and UV-B combined treatment group (B + L ), Normal light group (CK). Since the flavonoids carry some unsubstituted hydroxyl or glycosyl groups, it is a polar compound. By the 'similar compatibility' principle, they have some level of solubility in polar solvents, such as methanol, ethanol, n-butanol, propanol, and water. In this experiment, 70% ethanol was used to extract flavonoids. Finally, the total content of flavonoids in mesophyll cells was determined by visible spectrophotometry. The OD value of flavonoids was determined by rutin reagent 'The standard curves because rutin is a representative of flavonoids, it scavenging the role of free radicals significantly. The results showed that when the UV-B UV radiation intensity was enhanced, the content of flavonoids in wheat mesophyll cells increased, that is, the content of flavonoids in wheat leaves was higher than that in UV-B Strength was positively correlated. The results showed that the content of flavonoids in the mesophyll cells of the four control groups was the same as that of the B group> BL group> CK group> L group. With the prolonging of the treatment time of wheat, the content of flavonoids in wheat leaves at jointing-booting stage was significantly higher than that in seedling stage and panicle stage. This means that flavonoids are a protective substance that absorbs UV-B in plants, that is, the absorption of UV-B by flavonoids reduces the damage of UV-B to organs in plants [8] [10]; UV-B The smaller the damage, the less the content of flavonoids; laser damage caused by UV-B have a certain role in the repair. In this study, we further studied the effect of enhanced UV-B radiation on the content of flavonoids in mesophyll cells of wheat. The effects of UV-B radiation on the content of flavonoids in wheat were studied. Whether it has a very important significance for wheat has become a stress [5].

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.