This paper presents a comprehensive solution to the critical problem of vegetativity prediction by utilizing advanced time series models to enhance accuracy and reliability. As environmental monitoring and agricultural management become increasingly vital, our work aims to provide precise insights into vegetation health and growth patterns while addressing challenges posed by atmospheric conditions. We focus on developing an innovative filter designed to manage images affected by cloudiness. A significant achievement of our research is achieving over 95% classification accuracy for distorted images using convolutional neural networks (CNNs). This accuracy demonstrates the model's effectiveness in identifying and classifying vegetation types, even with compromised image quality. Additionally, our ensemble of neural networks yields a normalized difference vegetation index (NDVI) prediction error of less than 0.1, crucial for stakeholders dependent on accurate NDVI values for assessing plant health and managing agricultural practices. Our algorithms are practically applicable, especially in differential fertilizer application, enabling tailored fertilizer use based on the unique vegetative needs of specific areas. This approach optimizes resource utilization and fosters sustainable agriculture. We also provide expressions for calculating NDVI and techniques for smoothing indices in case of missing data, ensuring continuity in monitoring.Moreover, we tackle the challenge of cloud image filtering to improve data clarity. These algorithms have broader applications, such as fire monitoring, where accurate data are essential. Overall, our findings offer valuable resources for specialists in remote sensing, promoting better resource management and sustainable practices through innovative machine learning techniques.