气候变化领域集成服务门户(CCPortal): A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

CCPortal

DOI	10.3390/plants13050746
	A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data
发表日期	2024
ISSN	2223-7747
起始页码	13
结束页码	5
卷号	13 期号:5
英文摘要	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.
英文关键词	tomato quality; extreme gradient boosting; artificial neural network; prediction; shapley additive explanations
语种	英语
WOS研究方向	Plant Sciences
WOS类目	Plant Sciences
WOS记录号	WOS:001183001900001
来源期刊	PLANTS-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298859
作者单位	Hungarian University of Agriculture & Life Sciences; Hungarian University of Agriculture & Life Sciences; Universite de Carthage
推荐引用方式 GB/T 7714	. A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data[J],2024,13(5).
APA	(2024).A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data.PLANTS-BASEL,13(5).
MLA	"A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data".PLANTS-BASEL 13.5(2024).

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