# A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

*PRI Rank #8 · Topics and Trends in Most Cited Horticultural and Viticultural Research Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/horticultural-and-viticultural-research/2026/rank-8*

| Field | Value |
| --- | --- |
| Rank | #8 |
| 18m citations | 29 |
| Journal | Plants |
| Year | 2024 |
| DOI | 10.3390/plants13050746 |
| Corresponding authors | Sándor Takács |
| Institution | Hungarian University of Agriculture and Life Sciences, Hungary |

**Ranking page:** [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)

**Paper link:** [10.3390/plants13050746](https://doi.org/10.3390/plants13050746)

## 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

## Cite this ranking

```
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
```