## 3 Results  
Through a comprehensive analysis of environmental factors and various vegetation indexes, this study aims to determine which aspects of the maize growth stage and other factors can be adjusted to predict seed weight. In the first part of the study, a total of seventeen UAV flights were conducted to collect images from a bird's-eye view, creating a detailed map of the study area. The images' data from each day were then computed for twenty-four vegetation indexes with both the CD and CV, namely the Clgr_CD, Clre_CD, EV12_CD, GNDVI_CD, MCAR2_CD, MTVI2_CD, NDRE_CD, NDVI_CD, NDWI_CD, OSAVI_CD, RDVI_CD, RGBVI_CV, Clgr_CV, Clre_CV, EV12_CV, GNDVI_CV, MCAR2_CV, MTVI2_CV, NDRE_CV, NDVI_CV, NDWI_CV, OSAVI_CV, RDVI_CV, and RGBVI_CD. The goal was to identify which specific days of the growth stage and vegetation indexes show high correlations with seed weight. The results are presented in Figure 5, which highlights five dates and five vegetation indexes (VIs) that displayed a strong correlation with seed weight. These included day 56 of stage V10, days 65 and 72 of stage R1, and day 79 of stage R2, and day 85 of stage R3. Additionally, the VIs with the highest correlation were the Clre_CD, NDRE, Clgr, EV12, and NDVI. Notably, on day 79 of stage R2, the VI indexes Clre, NDRE, Clgr, EV12, and NDVI demonstrated particularly high correlations of 0.80, 0.80, 0.77, 0.75, and 0.74, respectively. Forty features with two hundred seventy data plots on day 79 of stage R2 were selected to feed the ML algorithm to predict grain yield. In this study, all ground truth data were gathered simultaneously on a singular date and time during the unmanned aerial vehicle (UAV) flight missions. The collected data were bifurcated into two distinct categories: the first encompassing the soil properties' data, which entailed NPK nutrient levels, pH, EC, soil temperature, soil humidity, water feed per area (m\({}^{3}\)/dunam), and maize height (m) and the second comprising weather data, including ultraviolet radiation (UV), evapotranspiration, daily rain, rain rate, humidity, wind speed, and temperature. The weather-related data were consistently captured at five-minute intervals throughout the entire growing season. Figure 6 depicts the correlation between seed weight and sixteen factors derived from the soil properties and weather data used for the analysis. Daily rain and rain rate were excluded from this correlation analysis due to their predominantly negligible values throughout the entire growing season, with most observations indicating zero or minimal rainfall. Among the various factors examined, several displayed a modest correlation with seed weight. These factors encompassed maize height, temperature, humidity, wind speed,  
UV radiation, water feed, and evapotranspiration, exhibiting correlation coefficients of 0.32, 0.28, 0.26, 0.25, 0.19, 0.19, and 0.17, respectively. Figure 5: Five high correlation of date of growth stage and VIs with seed weight. Green shades indicate positive correlations, darker shades suggest stronger relationships, red shades suggest negative correlations, darker shades suggest stronger inverse relationships, and neutral shades indicate little to no correlation. Figure 6: Sixteen environment data to correlate with seed weight. For the daily rain and rain rate, the values are zero and do not appear in correlation chart. Green shades indicate positive correlations, darker shades suggest stronger relationships, red shades suggest negative correlations, darker shades suggest stronger inverse relationships, and neutral shades indicate little to no correlation. To facilitate the analysis, the weather data obtained on day 79, corresponding to stage R2 or 30 March 2023, during the time interval from 10.00 a.m. to 2.00 p.m., were averaged. These weather data were collected using Internet of Things (IoT) sensors and included measurements of the temperature (39 \({}^{\circ}\)C), rainfall (0.0 mm), UV index (7.2 UV), and evapotranspiration rate (4.8 mm).