The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), -Nearest Neighbors (-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

Deep learning models classify peanut size accurately under fixed imaging conditions, but accuracy drops when apparent object scale changes, for example with camera-to-object distance. Ordinal losses and feature-level fusion are rarely compared under a leakage-controlled protocol. To address both gaps, this study evaluates a single pipeline that pairs scale-aware training with morphological feature fusion. Five backbone architectures (ViT-Base, DeiT-Small, Swin-Tiny, ResNet-50, EfficientNet-B0) were trained with three loss functions (cross-entropy, CORAL, CORN). Continuous isotropic scale augmentation (50–$150\%$) simulated the scale change expected in the field, and robustness was measured with a ten-step synthetic scale scan against a non-augmented control. Independently trained CORAL and CORN outputs were then stacked with analytically rescaled morphological features, to test whether fusion adds genuine value beyond the base models. In the resulting comparison, transformer backbones achieved higher classification accuracy than convolutional networks, and CORN was the most stable of the three loss functions. Scale augmentation delivered a clear robustness gain at a modest cost: for ViT-Base with CORN, mean accuracy across the tested scale range rose from $43.3\%$ to $88.5\%$, and cross-scale variability fell sevenfold, with only a small loss at native scale. The same pattern held for Swin-Tiny ($42.9\%$ to $89.2\%$, a $7.6$-fold reduction) and ResNet-50 ($43.4\%$ to $80.3\%$, a $5.4$-fold reduction). Gradient-boosting fusion then raised accuracy by $7.0$ percentage points on average over the best single model, with the largest gains on the weaker convolutional backbones. Permutation importance and boundary-case analysis confirmed that this gain did not rest on a single dominant size-correlated feature. Scale-aware training and analytically consistent fusion, rather than architectural complexity, therefore account for the main improvement in image-based ordinal size grading.