## 1 Introduction
Hence, the issue of overtraining is deemed inconsequential [27]. However, reducing the size of m has the effect of decreasing the inter-tree correlation, but at the cost of diminishing the predictive capability of an individual tree. Enhancing the depth of an individual tree amplifies its predictive capacity and concurrently augments the connection among trees [28]. Traditional approaches for growth monitoring involve obtaining measurements through field measurement techniques. The process of manually observing crop growth is characterized by its labor-intensive nature, inefficiency, and the need for a significant level of experience and agricultural knowledge on the part of the observer. This means that the results of manual monitoring methods are very subjective, which means that they are not good for countries like China that have complicated terrains and a lot of crops spread out. Instead, remote sensing methods are better for keeping an eye on the physiological and biochemical conditions of crops and guessing how much food they produce because they are quick, accurate, and do not damage the plants. Since the 1980s, foreign scholars have been at the forefront of initiating research on agricultural growth monitoring through the use of remote sensing technology. Using Landsat data with a high temporal resolution, [PERSON] et al. [29] created a vegetation index growth curve for crops. This showed how the crops were growing. In their study, [PERSON] et al. [30] employed National Oceanic and Atmospheric Administration (NOAA) data to observe the growing conditions of crops. They highlighted that multi-temporal NDVI data has the ability to indicate several aspects related to crop productivity. In particular, they stressed how important the cumulative NDVI value is as a key indicator for predicting biological output. The utilization of NDVI data enables the inverse analysis of covariates that indicate crop productivity. Among these covariates, the cumulative NDVI value plays a crucial role in forecasting biological output. A study conducted by [PERSON] et al. [31] used the greenness vegetation index to look at how crops were growing after a locust disaster in the Sahel region of Africa. The parties involved acknowledged the early identification of the impending tragedy relating to food scarcity. [PERSON] et al. [32] conducted a study using the Advanced Very High Resolution Radiometer NDVI (AVHRR-NDVI) to see how well the vegetation index recession model could be used to track the growth of cash crops. The researchers also confirmed the effectiveness of NDVI in monitoring the growth and estimating the yearly yield of cotton. [PERSON] et al. [33] used a quantitative growth indicator called Time-integrated NDVI (TINDVI) to look at time-series curves made from AVHRR-NDVI data from more than one year. Their study aimed to elucidate the various sensitivity responses of crop yields to precipitation in the context of western Australia. [PERSON] et al. [34] conducted a study attempting to find a strong linear link between VDRVI values in maize during the silking stage and the final grain yield. This examination was carried out at both the field and regional levels across many states in the United States. To monitor the growth progress, the researchers utilized MODIS-WDRV as an indicator. At present, there is a limited body of research that integrates cloud platforms and machine learning techniques to extract maize data at the city level. When it comes to the extraction of crop area and monitoring of growth, a majority of studies have opted for the utilization of MODIS image data for analysis and monitoring purposes. However, the limited spatial resolution of MODIS data remains a challenge in the application of remote sensing technology for yield prediction. This limitation has a detrimental effect on the accuracy of prediction results [35]. In the context of MODIS multiday composite picture data, it is observed that the resulting images exhibit a combination of diverse features.