## 4 Discussion  
### Validating the Model with Unseen Data  
In practical terms, the conventional breeder's approach to yield prediction involves random sampling, as illustrated for each plot in Table 9.  
For Plot 1, covering an area of 24,000 m\({}^{2}\) and hosting 8960 maize plants, a sampled cob contained 360 seeds. The weight of 100 seeds was 26 g, and the entire cob weighed 93.6 g. This translates to 3846 seeds per kilogram. Moving on to Plot 2, spanning 33,600 m\({}^{2}\) with a slightly larger population of 9173 maize plants, a cob from this plot carried 288 seeds. The weight measurements indicated 21 g for 100 seeds and 60.48 g for the full cob. Here, one kilogram is equivalent to 4762 seeds. Finally, Plot 3, occupying an area of 30,400 m\({}^{2}\), had the highest population among the three plots, housing 9813 maize plants. A single cob from this plot contained 300 seeds, with the weight of 100 seeds at 19.4 g and the entire cob at 58.2 g. The actual harvested weights for Plots 1, 2, and 3 were 1314.44 kg, 236.92 kg, and 387.82 kg, respectively. In this comparative analysis, we evaluated the performance of different approaches in predicting maize harvest yields, contrasting them with the traditional breeder's method, as illustrated in Table 10.  
The examined methods encompass a traditional breeder's approach, five ML models, and four ensemble methods denoted as PEsemble 1 through 4. To gauge accuracy, we computed the percentage of errors between the actual harvest yields and predictions for the three distinct plots with varying actual harvests: 1314.44 kg for Plot 1, 236.92 kg for Plot 2, and 387.82 kg for Plot 3. For Plot 1, where the actual harvest was 1314.44 kg, the traditional breeder's method predicted a yield of 503.19 kg, resulting in a substantial 62% error. Meanwhile, the Huber model predicted 986.23 kg with a more moderate 25% error, and the M estimators model also predicted 991.44 kg with a 25% error. The linear regression and ridge regression models forecasted 998.47 kg and 999.03 kg, respectively, both exhibiting a 24% error. Additionally, the four PEsemble models provided predictions and errors ranging from 988.31 kg (25% error) to 990.61 kg (25% error). Moving to Plot 2, where the actual harvest was 236.92 kg, the traditional breeder's method predicted 332.87 kg, resulting in a substantial 40% error. Notably, models like Huber \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Actual Harvesting (kg)**} & \multirow{2}{*}{**Plot 1: 1314.44**} & \multicolumn{2}{c}{**\% of Error for**} & \multicolumn{1}{c}{\multirow{2}{*}{**Plot 2: 236.92**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Plot 3: 387.82**}} & \multicolumn{1}{c}{\multirow{2}{*}{**\% of Error for**}} \\  & & & & & **Plot 2** & \\ \hline Breeder & 503.19 & 0.62 & 332.87 & 0.40 & 342.67 & 0.12 \\ Huber & 986.23 & 0.25 & 258.20 & 0.09 & 345.22 & 0.11 \\ M estimators & 991.44 & 0.25 & 259.58 & 0.10 & 346.97 & 0.11 \\ Linear regression & 998.47 & 0.24 & 262.01 & 0.11 & 348.57 & 0.10 \\ Ridge regression & 999.03 & 0.24 & 263.05 & 0.11 & 348.01 & 0.10 \\ PEsemble 1 & 990.30 & 0.25 & 259.48 & 0.10 & 346.36 & 0.11 \\ PEsemble 2 & 990.50 & 0.25 & 259.45 & 0.10 & 346.50 & 0.11 \\ PEsemble 3 & 990.61 & 0.25 & 259.65 & 0.10 & 346.39 & 0.11 \\ PEsemble 4 & 988.31 & 0.25 & 258.75 & 0.09 & 345.92 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A comparison of the percentage error between the actual harvest grain yield and the predictions from various approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Plot Name**} & **No. of Seeds** & **Weight of** & **Weight of** & **No. of Seeds** & **Actual** \\  & **with One** & **100 Seeds (g)** & **Seeds with** & **per One** & **Harvesting** \\  & **Cob** & **One Cob (g)** & **Kilogram** & **(kg)** \\ \hline
1 & 360 & 26 & 93.6 & 3846 & 1314.44 \\
2 & 288 & 21 & 60.48 & 4762 & 236.92 \\
3 & 300 & 19.4 & 58.2 & 5155 & 387.82 \\ \hline \hline \end{tabular}
\end{table}