## 4 Discussion  
This study aimed to obtain a substantial quantity of remote sensing image data from the GEE cloud platform. Specifically, Sentinel-2 image data with a spatial resolution of 10 m were utilized to acquire the image dataset of Sentinel-2 in Huinong District, spanning the years 2017 to 2021. The selection of this dataset took into account the operational cost associated with monitoring large-scale maize areas and the accuracy of information extraction. To extract information from the dataset, support vector machines and random forest methods were employed. The labeled images were constructed using the results that exhibited better levels of accuracy. After conducting a comparison, it was observed that the random forest algorithm exhibited a 2.3% increase in overall accuracy when compared to the support vector machine algorithm. This increase signifies a higher percentage of correctly classified image elements. In addition, the Producer Accuracy went up by 43.3%, which means that there is a much higher chance of obtaining consistent classification results for certain spots on the classification map. Furthermore, the User Accuracy showed a significant improvement of 47.5%, indicating a large rise in the chance of correctly matching the type of a random sample with the actual surface conditions. Additionally, the Kappa coefficient showed a positive increase of 0.232, showing that the random forest classification method is very good at correctly identifying maize. This method exhibits high classification accuracy and effectively fulfills the requirements of precision agriculture through remote sensing monitoring [50; 51]. The researchers opted for the random forest classification technique to delineate the maize cultivation region inside Huinong District throughout the period spanning from 2017 to 2021. It was found that the extracted maize planting area in this study has fewer mixed-up parts, better average accuracy when tested using the confusion matrix computation, and a fairly small overall difference error when compared to the statistical data. While this study demonstrates an overall improvement in the extraction impact, it is important to note that there remains a large mistake in accurately extracting mountains and highways. The subsequent research direction entails employing the uncrewed aerial vehicle (UAV) platform to integrate high spatial resolution images with high temporal resolution images for the purpose of fusion. Additionally, it involves optimizing the original spectral features and utilizing deep learning methods to extract the maize planting area within a larger region. The present work employed a random forest approach to accurately identify maize crops in the Huinong District using synthetic photos from the Sentinel-2 satellite. In order  
Figure 10: Average temperature and average sunshine for May–September: (**a**) describes the average temperature for the months of May–September, 2017–2021; (**b**) describes the average number of hours of sunshine for the months of May–September, 2017–2020. to monitor maize development in the research area, a mask was created for the extracted region. This mask was then utilized to combine the masked area with MODIS image data. The NDVI difference model was employed for this purpose. The conducted experiments demonstrate that the methodology employed in this investigation has the capability to yield precise data regarding the spatial arrangement of winter wheat cultivation as well as the frequent monitoring of its growth patterns. Further enhancements are required in the methodology employed for tracking the growth of maize in this study. The Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) synthetic picture dataset was used in this study. The time resolution was ten days. The primary objective was to conduct a comparative analysis between the current year's NDVI values and those of both the subsequent year and preceding years.