## 1 Introduction
### Our Main Contribution  
To the best of the authors' knowledge, this paper presents a novel and comprehensive approach that significantly differs from existing works in predicting crop yields. While previous studies have explored various methodologies, such as statistical modeling [26], and deep learning techniques [27; 28], they have yet to integrate multi-temporal images and ML algorithms in the manner proposed here.  
By integrating remote sensing data with ML techniques, our study offers a practical and innovative framework for forecasting crop yields. The incorporation of multi-temporal images allows for a holistic understanding of crop growth patterns and their response to changing environmental conditions over time, enabling more precise predictions.  
By harnessing the power of various vegetation indices and employing ML algorithms, our research achieves remarkable accuracy in forecasting crop yields. This advancement holds significant implications for enhancing global food security and optimizing crop management practices, as it empowers farmers with valuable insights for making informed decisions.  
Moreover, our study contributes to the expanding body of research on the integration of ML and remote sensing in agriculture, emphasizing the importance of interdisciplinary collaborations between the fields of agriculture and technology. This interdisciplinary synergy has the potential to revolutionize agricultural practices and pave the way for a more sustainable and productive future in the face of increasing challenges in the agricultural sector.  
The objectives of our study can be summarized as follows:  
* To study factors impacting the life cycle of maize, including the pre-planting stage, growth stage, and post-production stage in maize fields.
* To collect and analyze environmental factors, including evapotranspiration, rain rate, wind speed, temperature, humidity, NPK nutrients, and pH.
* To calculate 12 vegetation indices, including the Chlorophyll Index green (CIgr), Chlorophyll Index red edge (CIre), Enhanced Vegetation Index 2 (EVI2), Green Normalized Difference Vegetation Index (GNDVI), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), Modified Triangular Vegetation Index 2 (MTVI2), Normalized Difference Red Edge (NDRE), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Renormalized Difference Vegetation Index (RDVI), and Red-Green-Blue Vegetation Index (RGBVI) with multispectral (MS) imagery from the UAVs equipped with MS cameras.
* To predict the growth stage and productivity of maize using ML regression techniques [29; 30] including CatBoost regression, decision tree regression, ElasticNet regression, gradient boosting regression, Huber regression, K-Nearest Neighbors (KNN) regression, LASSO regression, linear regression, M estimators, passive aggressive regression, random forest (RF) regression, ridge regression, Support Vector Regression (SVR), and XGBoost regression algorithms.  
The paper is structured as follows: Section 2 provides a comprehensive description of the materials and methods used in the study, including the study locations, climate conditions, soil characteristics, and agricultural practices. Section 3 details the specific approaches and techniques employed. Section 4 presents the key findings, addressing the strengths and limitations of the proposed approach. Finally, in Section 5, the principal conclusions are summarized, highlighting the practical implications for precision agriculture and sustainable crop management.