Ercan Uğur, Sonmez Ilker, Kabaş Aylin, Kabas Onder, Calık Zyambo Buşra, Gölükcü Muharrem, Paraschiv Gigel
Department of Informatics, Akdeniz University, 07070 Antalya, Türkiye.
Department of Soil Science and Plant Nutrition, Faculty of Agriculture, University of Akdeniz, 07070 Antalya, Türkiye.
Foods. 2024 Nov 29;13(23):3858. doi: 10.3390/foods13233858.
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 (R) 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 R: 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 R: 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.
本文使用支持向量回归（SVR）和多元线性回归（MLR）模型方法，展示了嫁接不同品种砧木的甜瓜果实的糖度含量。分析得出了以下砧木（狮身人面像、信天翁和迪内罗）果实的主要生化测量值：氮、磷、钾、钙和镁。使用平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方误差（MSE）、均方根误差（RMSE）和决定系数（R）指标对建立的模型进行评估。在测试部分，MLR模型的结果计算为MAE：0.0728，MAPE：0.0117，MSE：0.0088，RMSE：0.0936，R：0.9472，而SVR模型的结果计算为MAE：0.0334，MAPE：0.0054，MSE：0.0016，RMSE：0.0398，R：0.9904。尽管两个模型都表现良好，但SVR模型显示出更高的准确性，在预测方面比MLR模型高出54%至82%。研究发现糖度水平与各种营养物质（如蔗糖、葡萄糖和果糖）之间的关系很强，而可滴定酸度的影响最小。SVR被发现是一种更可靠的、非破坏性的甜瓜品质评估方法。这些发现揭示了糖度与甜瓜品质中糖分水平之间的关系。该研究突出了这些机器学习模型在优化砧木效应和管理甜瓜种植以提高果实品质方面的潜力。
