## 4 Discussion
The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios with scatter index (SI) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). Our research outcomes disagree with [PERSON], [PERSON] [63], where the highest RMSE values were obtained under single and hybrid ML models with RF and RF-XGB-SVR at 19.55 and 19.24 and Sc3 (remote sensing indices), while the lowest RMSE value was detected under XGB-SVR hybrid model and Sc2 (Peeff, Tmax, Tmin, and SA) at 3.28. In the same context, which also supports the outputs of individual machine learning programmes, [PERSON], [PERSON] [64] developed and compared four machine learning models--SVR, RF, XGB, and ANN--over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt to select the best model in the best combination of climate input variables to predict potato BWFP during 1990-2016. The available variables used were Tmax, Tmin, Tave, WS, RH, P, VPD, SR, SA, and Kc. Six scenarios (Scl-Sc6) of input variables were used. The findings indicated that Sc5, utilizing the XGB and ANN models, exhibited the most promising outcomes in predicting BWFP in a dry region by considering vapour pressure deficit, precipitation, solar radiation, and crop coefficient data, followed by Sc1 (incorporating all parameters). These developed models yielded notably superior results, offering valuable insights for water management and development planning decisions. The research results agree with a part of the results of [PERSON], [PERSON] [64], which deals with the superiority of the single model over hybrid ML models where the single model achieved the lowest RMSE and highest R values followed by hybrid models, but they disagree with the part of best scenario where Sc4 (Pe, Tmax, Tmin, and SA) achieved the highest R\({}^{2}\) value with 0.98 like the Sc2 (all climatic variables). This supports the use of climate indicator monitoring stations with the least possible number of indicators and thus at the lowest price, which benefits developing countries with limited financial capabilities. [PERSON], [PERSON] [65] employed the XGBoost regression (XGBR) model to estimate ET over three years (2019-2021), focusing on the impact of various meteorological factors on ET. Their study utilized a greenhouse drip-irrigated tomato crop ET prediction model (XGBR-ET) based on XGBoost regression, comparing it with seven other regression models. The importance of meteorological factors on XGBo-ET was ranked as follows: Rn > RH > RHmin > Tmax > RHmax > Tmin > Ta > VPD. Performance evaluation metrics R\({}^{2}\), RMSE, and MAE were reported as 0.981, 0.163, and 0.132, respectively. Our results are not in line with those of [PERSON], [PERSON] [65], where the feature importance raked as follows: Pe\({}_{\text{eff}}\) > RH > Tmax > Kc > Tave > Tmin > Rn > WS > SA. This is related to the fact that GWFP mainly depends on Pe\({}_{\text{eff}}\), which recorded the highest value in feature importance with 81.67%, followed by relative humidity (RH) with a 7.5% impact. To optimize the SVR model, [PERSON] et al. [66] used particle swarm optimization (PSO), RF, and SVR. Using various input meteorological variables, the hybrid RF-SVR-PSO model was assessed against a standalone SVR model, a back-propagation neural network (BPNN) model, and an RF model. All models were used to estimate ETc; the best model was SVR, with an R\({}^{2}\) of 0.97, followed by the hybrid RF-SVR-PSO model, with an R\({}^{2}\) of 0.975.