ArticleHeliyon2025
Data science for pattern recognition in agricultural large time series data: A case study on sugarcane sucrose yield.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Multidimensional Perspective on the Quality of Green Onion: Between the Visible, the Measurable, and the Perceived.Food science & nutrition · 2026Article
- Selection for high-yielding sugarcane ideotypes and multi-trait selection via MGIDI across different harvesting periods improves sugar quality and yield.Scientific reports · 2026Article
- ThermoMicrowave-sonication improves the stability and digestive bioaccessibility of phenolic compounds in parsley juice.Food chemistry: X · 2026Article
- Edaphic homologous zones and digital tools as a basis for sustainable soil management in potato growing areas in Colombia.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data science (DS) is one of the areas with the greatest versatility for application in any field of knowledge. It allows the optimization of different processes of daily life and permits the analysis of massive amounts of data. DS combines computer programming with mathematics and statistics tools in multiple environments such as Python, Julia, R, among others. Here, a protocol was proposed to use DS tools applied to the organization, visualization, and analysis of historical data in sugarcane production systems in the tropics as a basis to identify patterns associated with sucrose. The protocol consisted of four phases: (i) data collection and organization, (ii) data management, cleaning and incorporation of new variables, (iii) visualization tools, and (iv) analysis and modeling based on a multiapproach using a frequentist model (generalized lineal model), a regularized regression model (Lasso) and machine learning models (AutoML). Each of the phases was implemented using multiple algorithms and techniques to automate processes such as queries, numerical calculations, sorting, grouping, dividing, pivoting, totalizing, concatenation, cleaning, visualization, and fitting to models using the free Python software and libraries including
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.