A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data
Most Cited Paper
in Horticultural and Viticultural Research
18 Months Post-Publication
Class of 2026
“A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/horticultural-and-viticultural-research/2026/
Topics
XGBoost · artificial neural network · tomato fruit quality prediction · Brix · lycopene content · a/b ratio · SHAP analysis · cultivar selection · environmental factors · Meteorological data · precision agriculture · machine learning in agriculture · multi-location field trials · tomato processing quality · climate influence on fruit quality · soil factors
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Horticultural and Viticultural Research Papers, Class of 2026. https://pri.pepkio.com/top-papers/horticultural-and-viticultural-research/2026. Accessed 2026-07-22. Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459