外文摘要：The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on degrees Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting degrees Brix (R-2 = 0.98, RMSE = 0.07) and lycopene content (R-2 = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R-2 of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R-2 value of -0.35. Shapley additive explanation's (SHAP) summary plot analysis indicated that both models are effective in predicting degrees Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models' efficiency (especially in degrees Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost's superiority in handling complex agronomic data for quality assessment.
外文关键词：artificial neural network;prediction;Extreme gradient boosting;tomato quality;shapley additive explanations
作者：M'hamdi, Oussama;Takacs, Sandor;Palotas, Gabor;Ilahy, Riadh;Helyes, Lajos;Pek, Zoltan
作者单位：Hungarian Univ Agr & Life Sci;Univ Carthage;Univer Prod Zrt
期刊名称：PLANTS-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：13(5)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台