The article demonstrates the Brix content of melon fruits grafted with different varieties of rootstock using Support Vector Regression (SVR) and Multiple Linear Regression (MLR) model approaches. The analysis yielded primary fruit biochemical measurements on the following rootstocks, Sphinx, Albatros, and Dinero: nitrogen, phosphorus, potassium, calcium, and magnesium. Established models were evaluated with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) metrics. In the test section, the results of the MLR model were calculated as MAE: 0.0728, MAPE: 0.0117, MSE: 0.0088, RMSE: 0.0936, and R²: 0.9472, while the results of the SVR model were calculated as MAE: 0.0334, MAPE: 0.0054, MSE: 0.0016, RMSE: 0.0398, and R²: 0.9904. Despite both models performing well, the SVR model showed superior accuracy, outperforming MLR by 54% to 82% in terms of predictions. The relationships between Brix levels and various nutrients, such as sucrose, glucose, and fructose, were found to be strong, while titratable acidity had a minimal effect. SVR was found to be a more reliable, non-destructive method for melon quality assessment. These findings revealed the relationship between Brix and sugar levels on melon quality. The study highlights the potential of these machine learning models in optimizing the rootstock effect and managing melon cultivation to improve fruit quality.

This study aimed to investigate the effects of a liquid fraction of digestate obtained from different biogas plants on the growth and mineral nutrition of cucumber plants under greenhouse conditions. For this purpose, Liquid Biogas Digestates (LBD) obtained from two different plants (A-B) with different properties were applied to pots with 10 kg of soil in 5 different doses (0, 20, 40, 60, and 80 t ha-1) and the effects of the treatments were observed. As a result of the research, the highest yields increased 24.6% for digestate A in A5 (80 t ha-1) and 29% for digestate B in B3 (40 t ha-1) compared to control. While LBD contributed to the increase of N, Ca, Zn, Cu, and Mn concentrations in the leaf samples, it was observed that the dose increase did not have a linear effect on N, Ca, Zn, Cu, and Mn concentrations in the leaf samples. It is thought that liquid biogas wastes produced in biogas plants have positive effects on fruit yield, agricultural practices can be taken the basis in the disposal of these wastes and the use of liquid biogas residues in soils by eliminating potential risks can provide significant benefits.