## 2 Materials and Methods
### Selection of Machine Learning (ML) Methods for Grain Yield Predictions  
In the domain of machine learning (ML) in precision agriculture, the authors present a comprehensive approach, as illustrated in Figure 4, to identify the most suitable model for crop yield prediction. The study explores the efficacy of 14 distinct ML algorithms in predicting the growth stage and productivity of maize. These algorithms encompass CatBoost regression [48], decision tree regression [49; 50], ElasticNet regression [51], gradient boosting regression [52; 53], Huber regression [54], K-Nearest Neighbors (KNN) regression [55], LASSO regression [56], linear regression [57], M estimators [58], passive aggressive regression, random forest (RF) regression [59], ridge regression, Support Vector Regression (SVR) [60], and XGBoost regression [61]. By employing this diverse range of algorithms, the authors aimed to optimize the accuracy and reliability of crop yield prediction in the context of precision agriculture. The proposed flow chart encompasses two integral components. The first component involves the analysis of multi-temporal images, as explicated in Section 2.3, wherein a comprehensive examination is conducted on various vegetation indices, chlorophyll contents, and other pertinent features. A total of 17 imagery datasets, acquired from 17 flights, as delineated in Table 2, underwent meticulous scrutiny, resulting in the extraction of 4590 ROIs derived from the multiplication of 270 ROIs by 17 days. These carefully selected ROIs were seamlessly integrated with 14,195.tiff images utilizing the _DJI Terra_ software version V4.0.10, thereby establishing pixel-to-ROI correspondences. The ROIs were subjected to rigorous processing, incorporating precise geographic coordinates obtained through Ground Control Points (GCPs) following established procedures. The extracted pixel data derived from these ROIs serve as the fundamental basis for computing VI-based canopy density, thereby enabling the classification of pixels into two distinct categories--green pixels and non-green pixels. By focusing exclusively on green pixels, the values of twelve VIs were computed for each ROI. Figure 3: Equipment used for the measurements in the research: (**a**) DJI Drone with multi-spectrum capability, (**b**) Vantage Pro2 weather station, (**c**) NPK Soil Analyzer, (**d**) our IoT design for soil health monitoring, and (**e**) prototype of smart trap. The computed VIs were subsequently employed to establish linear relationships with maize yields, aiming to investigate their correlation. Specifically, the VIs obtained during critical growth stages were utilized to calculate the coefficient of determination (R-squared, R2) values. These R2 values serve as indicators of the predictive capabilities exhibited by the various vegetation indices. By identifying the most effective indices during significant growth stages, valuable insights can be gained regarding growth monitoring, and more accurate yield predictions can be made. The second part of the flow chart focuses on the application of ML algorithms for crop yield prediction. Over the past decade, ML algorithms have demonstrated remarkable potential and have outperformed traditional methods in various domains, including object detection, image classification, pattern recognition, and others. Leveraging supervised learning techniques, these algorithms have proven to be highly effective. In the specific context of crop yield prediction, the trained ML models were evaluated by assessing their performance on independent test samples. To select the most suitable ML algorithm for crop yield prediction, a careful selection process was undertaken. This involved considering a range of 14 ML algorithms, chosen based on their prominence in the literature and their distinctive characteristics. The goal was to identify the algorithm that best aligns with the specific requirements of predicting the growth stage and productivity of maize.